data_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os \n",
        "\n",
        "os.chdir('app/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tWNdqSvEWXit"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "path_to_data = 'data'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "5hoM3WRrZEMc"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv(path_to_data + '/train.csv', usecols=['client_id', 'gender'])\n",
        "mcc_codes = pd.read_csv(path_to_data + '/mcc_codes.csv', sep=';')\n",
        "test_sample_submission = pd.read_csv(path_to_data + '/test_sample_submission.csv', usecols=['client_id', 'probability'])\n",
        "test = pd.read_csv(path_to_data + '/test.csv', usecols=['client_id'])\n",
        "trans_types = pd.read_csv(path_to_data + '/trans_types.csv', sep=';')\n",
        "transactions = pd.read_csv(path_to_data + '/transactions.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRWnMXcXZWbC",
        "outputId": "ce643bb3-2f39-4748-e8f6-6f13cd44f961"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Size of datasets:\n",
            "\n",
            "train (7560, 2)\n",
            "mcc_codes (184, 2)\n",
            "trans_types (155, 2)\n",
            "test (840, 1)\n",
            "transactions (3563529, 7)\n"
          ]
        }
      ],
      "source": [
        "print('Size of datasets:\\n')\n",
        "print('train', train.shape)\n",
        "print('mcc_codes', mcc_codes.shape)\n",
        "print('trans_types', trans_types.shape)\n",
        "print('test', test.shape)\n",
        "print('transactions', transactions.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Qj7-UPqf2rgd",
        "outputId": "7530cdb1-6e52-40bf-c643-7659c7f22516"
      },
      "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>client_id</th>\n",
              "      <th>trans_time</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>35 08:24:41</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>81c7349f7bdcb661bc7d5f5f0fdac6aa</td>\n",
              "      <td>351 13:27:15</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>442207</td>\n",
              "      <td>Kazan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fc0c7c937f139bd6d1c5b5d0a0023dd3</td>\n",
              "      <td>150 16:03:59</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-12293.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Khabarovsk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3495487d5beaa5caaa0682fddc38379a</td>\n",
              "      <td>373 11:54:12</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>49.52</td>\n",
              "      <td>888987</td>\n",
              "      <td>Kaliningrad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07e279042b1da23710fa2f65034d30b9</td>\n",
              "      <td>340 13:11:45</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>45.29</td>\n",
              "      <td>888918</td>\n",
              "      <td>Saint Petersburg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          client_id    trans_time  mcc_code  trans_type  \\\n",
              "0  d1bbbc9a0e0410d3cf12a3d2f44f3450   35 08:24:41      4829        2370   \n",
              "1  81c7349f7bdcb661bc7d5f5f0fdac6aa  351 13:27:15      6011        7010   \n",
              "2  fc0c7c937f139bd6d1c5b5d0a0023dd3  150 16:03:59      6011        2010   \n",
              "3  3495487d5beaa5caaa0682fddc38379a  373 11:54:12      6010        7070   \n",
              "4  07e279042b1da23710fa2f65034d30b9  340 13:11:45      6010        7070   \n",
              "\n",
              "     amount term_id        trans_city  \n",
              "0  -1808.56     NaN              Tver  \n",
              "1   7954.51  442207             Kazan  \n",
              "2 -12293.56     NaN        Khabarovsk  \n",
              "3     49.52  888987       Kaliningrad  \n",
              "4     45.29  888918  Saint Petersburg  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transactions.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd6UxQWIZv9v",
        "outputId": "5ce15e0a-e5be-4137-d45b-c85110e44bd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "duplicated values of transactions: 4\n",
            "--------------------\n",
            "null values of transactions:\n",
            " client_id           0\n",
            "trans_time          0\n",
            "mcc_code            0\n",
            "trans_type          0\n",
            "amount              0\n",
            "term_id       1443778\n",
            "trans_city          0\n",
            "amount_log          0\n",
            "income        2927703\n",
            "spent          635826\n",
            "dtype: int64\n",
            "--------------------\n",
            "null values of mcc codes:\n",
            " mcc_code           0\n",
            "mcc_description    0\n",
            "dtype: int64\n",
            "--------------------\n",
            "null values of transactions types:\n",
            " trans_type           0\n",
            "trans_description    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('duplicated values of transactions:', transactions.duplicated().sum())\n",
        "print('-'*20)\n",
        "print('null values of transactions:\\n', transactions.isnull().sum())\n",
        "print('-'*20)\n",
        "print('null values of mcc codes:\\n', mcc_codes.isnull().sum())\n",
        "print('-'*20)\n",
        "print('null values of transactions types:\\n', trans_types.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0D6jPDhaxgU",
        "outputId": "7a88f6fa-e0a5-4e39-b635-6456b31770c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of unique:\n",
            "8400\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "fbf94bb37e250669ea74b20ddaa89e0e    84313\n",
              "e91858185ae43cf3970220748893a01b    14404\n",
              "210a288dba0bd3bf6ce90f1f1b188951    13876\n",
              "7364b8067c0c257b45efd73c3d0a4028    11699\n",
              "07e279042b1da23710fa2f65034d30b9    11447\n",
              "e43f6f0ead88c8a17fc0543aa3e1599a    11442\n",
              "aba68a1204a662908cebfc09a1d5bbd3    10520\n",
              "f74c1398ead286f297e003b5e810ee99     9356\n",
              "8d69e4ecae4b509d528b9a0168f749d0     7998\n",
              "c20371cb1683ff049f277439b4aafb28     7660\n",
              "Name: client_id, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Number of unique:')\n",
        "print(transactions['client_id'].nunique())\n",
        "\n",
        "# top 10 clients who most often used the services of the bank\n",
        "transactions['client_id'].value_counts()[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top-20 MCC')"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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7tXz5cn311VfasGGDzpw5I3d3dwUFBalDhw566qmnFBQUpLi4uKL4V3LDJk+erAceeEC//vqr9u/fr4sXLyotLa1Y5wQAAAC4KjeTyWQq7kkAAAAAAAAAAADAOVnflRYAAAAAAAAAAAAoIjSjAAAAAAAAAAAA4DA0owAAAAAAAAAAAOAwNKMAAAAAAAAAAADgMDSjAAAAAAAAAAAA4DA0owAAAAAAAAAAAOAwNKMAAAAAAAAAAADgMDSjAAAAAAAAAAAA4DA0owAAAAAAAAAAAOAwNKMAAAAAAAAAAADgMDSjAAAAAAAAAAAA4DA0owAAAAAAAAAAAOAwNKMAAAAAAAAAAADgMDSjAAAAAAAAAAAA4DA0owAAAAAAAAAAAOAwNKMAAAAAAAAAAADgMP8f4RR3zV8zXwMAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "\n",
        "mcc_counts = df['mcc_code'].value_counts(ascending=False).iloc[:20]\n",
        "\n",
        "ax.barh(mcc_counts.index.astype('str'), mcc_counts, color='#D2DE26')\n",
        "ax.set_xlabel('Count', fontsize=24)\n",
        "ax.set_ylabel('MCC', fontsize=24)\n",
        "ax.set_title('Top-20 MCC', fontsize=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XTnW3ZK7tUIC"
      },
      "outputs": [],
      "source": [
        "def preproc(x):\n",
        "  if x > 0:\n",
        "    return np.log(1 + x)\n",
        "  return -np.log(1 - x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "rlv6HhCMtIl1"
      },
      "outputs": [],
      "source": [
        "transactions['amount_log'] = transactions['amount'].apply(preproc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "_R7kELhua3KQ",
        "outputId": "1cff9418-3196-4d56-ccd4-bff9e2b7eeb9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(20, 8))\n",
        "axes[0].set_title('Boxplot of transactions')\n",
        "labels = ['amount_log']\n",
        "axes[0].boxplot(transactions['amount_log'], labels=labels)\n",
        "\n",
        "axes[1].set_title('Histogram of transactions')\n",
        "\n",
        "axes[1].hist(transactions['amount_log'], bins=50, label = 'amount_log', color = '#182c6bff')\n",
        "axes[1].set_xlabel('amount_log')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "5MouFxbrcpQx"
      },
      "outputs": [],
      "source": [
        "def remove_outlier_IQR(df):\n",
        "    # IQR\n",
        "    Q1=df.quantile(0.25)\n",
        "    Q3=df.quantile(0.75)\n",
        "    IQR=Q3-Q1\n",
        "    # removing the outliers\n",
        "    df_without_outliers=df[~((df<(Q1-1.5*IQR)) | (df>(Q3+1.5*IQR)))]\n",
        "    # return data without outliers\n",
        "    return df_without_outliers\n",
        "\n",
        "data_with_no_outliers=remove_outlier_IQR(transactions['amount_log'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "e-p2aCz6A0qm",
        "outputId": "573913d6-2760-4d6d-ef83-cf26561f798a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, sharey=True, figsize=(15, 5))\n",
        "\n",
        "axes[0].set_title('Boxplot of transactions')\n",
        "\n",
        "labels = ['amount_log']\n",
        "axes[0].boxplot(transactions['amount_log'], labels=labels)\n",
        "axes[1].set_title('Boxplot of transactions without outliers')\n",
        "axes[1].boxplot(data_with_no_outliers, labels=labels)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "V57kyiezdAkp",
        "outputId": "77fd73e9-158e-4a6e-bb24-fc5a91f26253"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='mcc_description'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# services most often used by bank customers\n",
        "\n",
        "fig, axs = plt.subplots(ncols=1)\n",
        "fig.set_figwidth(15)\n",
        "\n",
        "data0 = transactions.merge(mcc_codes, on='mcc_code')\n",
        "data = data0.merge(trans_types, on='trans_type')\n",
        "\n",
        "# top 5 of transaction codes\n",
        "sns.barplot(x=data['mcc_description'].value_counts()[:5],y=data['mcc_description'].value_counts()[:5].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "ngwwjiiqpbPd",
        "outputId": "65f2add2-2636-4e08-d76f-d96abe01cc94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='trans_description'>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(ncols=1)\n",
        "fig.set_figwidth(15)\n",
        "\n",
        "# top 5 of transaction types\n",
        "sns.barplot(x=data['trans_description'].value_counts()[:5],y=data['trans_description'].value_counts()[:5].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UB6xTMjodmpH",
        "outputId": "71567448-5c3e-4fb3-cc55-eb18e873c51a"
      },
      "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>client_id</th>\n",
              "      <th>trans_time</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "      <th>amount_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>35 08:24:41</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-7.500839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>81c7349f7bdcb661bc7d5f5f0fdac6aa</td>\n",
              "      <td>351 13:27:15</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>442207</td>\n",
              "      <td>Kazan</td>\n",
              "      <td>8.981620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fc0c7c937f139bd6d1c5b5d0a0023dd3</td>\n",
              "      <td>150 16:03:59</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-12293.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.416912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3495487d5beaa5caaa0682fddc38379a</td>\n",
              "      <td>373 11:54:12</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>49.52</td>\n",
              "      <td>888987</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>3.922369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07e279042b1da23710fa2f65034d30b9</td>\n",
              "      <td>340 13:11:45</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>45.29</td>\n",
              "      <td>888918</td>\n",
              "      <td>Saint Petersburg</td>\n",
              "      <td>3.834926</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          client_id    trans_time  mcc_code  trans_type  \\\n",
              "0  d1bbbc9a0e0410d3cf12a3d2f44f3450   35 08:24:41      4829        2370   \n",
              "1  81c7349f7bdcb661bc7d5f5f0fdac6aa  351 13:27:15      6011        7010   \n",
              "2  fc0c7c937f139bd6d1c5b5d0a0023dd3  150 16:03:59      6011        2010   \n",
              "3  3495487d5beaa5caaa0682fddc38379a  373 11:54:12      6010        7070   \n",
              "4  07e279042b1da23710fa2f65034d30b9  340 13:11:45      6010        7070   \n",
              "\n",
              "     amount term_id        trans_city  amount_log  \n",
              "0  -1808.56     NaN              Tver   -7.500839  \n",
              "1   7954.51  442207             Kazan    8.981620  \n",
              "2 -12293.56     NaN        Khabarovsk   -9.416912  \n",
              "3     49.52  888987       Kaliningrad    3.922369  \n",
              "4     45.29  888918  Saint Petersburg    3.834926  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transactions.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Q5mgbkH_iUU3"
      },
      "outputs": [],
      "source": [
        "transactions.loc[transactions['amount'] > 0, 'income'] = transactions['amount']\n",
        "transactions.loc[transactions['amount'] <= 0, 'spent'] = transactions['amount']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RXTy2fBOijUk",
        "outputId": "b515c7da-67fd-491e-fe4a-f0799dee96be"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>trans_time</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
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              "      <th>amount_log</th>\n",
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              "      <th>spent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>35 08:24:41</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-7.500839</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1808.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>81c7349f7bdcb661bc7d5f5f0fdac6aa</td>\n",
              "      <td>351 13:27:15</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>442207</td>\n",
              "      <td>Kazan</td>\n",
              "      <td>8.981620</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fc0c7c937f139bd6d1c5b5d0a0023dd3</td>\n",
              "      <td>150 16:03:59</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-12293.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.416912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-12293.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3495487d5beaa5caaa0682fddc38379a</td>\n",
              "      <td>373 11:54:12</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>49.52</td>\n",
              "      <td>888987</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>3.922369</td>\n",
              "      <td>49.52</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07e279042b1da23710fa2f65034d30b9</td>\n",
              "      <td>340 13:11:45</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>45.29</td>\n",
              "      <td>888918</td>\n",
              "      <td>Saint Petersburg</td>\n",
              "      <td>3.834926</td>\n",
              "      <td>45.29</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          client_id    trans_time  mcc_code  trans_type  \\\n",
              "0  d1bbbc9a0e0410d3cf12a3d2f44f3450   35 08:24:41      4829        2370   \n",
              "1  81c7349f7bdcb661bc7d5f5f0fdac6aa  351 13:27:15      6011        7010   \n",
              "2  fc0c7c937f139bd6d1c5b5d0a0023dd3  150 16:03:59      6011        2010   \n",
              "3  3495487d5beaa5caaa0682fddc38379a  373 11:54:12      6010        7070   \n",
              "4  07e279042b1da23710fa2f65034d30b9  340 13:11:45      6010        7070   \n",
              "\n",
              "     amount term_id        trans_city  amount_log   income     spent  \n",
              "0  -1808.56     NaN              Tver   -7.500839      NaN  -1808.56  \n",
              "1   7954.51  442207             Kazan    8.981620  7954.51       NaN  \n",
              "2 -12293.56     NaN        Khabarovsk   -9.416912      NaN -12293.56  \n",
              "3     49.52  888987       Kaliningrad    3.922369    49.52       NaN  \n",
              "4     45.29  888918  Saint Petersburg    3.834926    45.29       NaN  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transactions.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "V-cw9NAcvWGi",
        "outputId": "183241b8-5de1-4e18-eb0a-37063ffd66c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f01327e6490>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# counting the number of transactions made per gender by code type\n",
        "\n",
        "train_gender = mcc_codes.copy(deep=True)\n",
        "train_gender['majority'] = 0\n",
        "\n",
        "data_train = pd.merge(transactions, train, on='client_id', how=\"right\")\n",
        "\n",
        "def calc_count_0(row):\n",
        "    return len((data_train[(data_train['mcc_code'] == row['mcc_code']) & (data_train['gender'] == 0)]))\n",
        "\n",
        "def calc_count_1(row):\n",
        "    return len((data_train[(data_train['mcc_code'] == row['mcc_code']) & (data_train['gender'] == 1)]))\n",
        "\n",
        "train_gender['0'] = train_gender.apply(lambda row: calc_count_0(row), axis=1)\n",
        "train_gender['1'] = train_gender.apply(lambda row: calc_count_1(row), axis=1)\n",
        "\n",
        "train_gender['coef'] = abs(train_gender['0'] - train_gender['1'])/(train_gender['0'] + train_gender['1'])\n",
        "\n",
        "train_gender['majority'] = train_gender['1'] > train_gender['0']\n",
        "\n",
        "plt.figure(figsize=(25,10))\n",
        "sns.set(font_scale=2,style='whitegrid')\n",
        "\n",
        "ax = sns.barplot(x=\"coef\", y=\"mcc_description\", data=train_gender.sort_values(by=['coef'], ascending=False).head(20),\n",
        "            hue='majority', palette={True:'#182c6bff', False:'#d2de26ff'}, dodge=False)\n",
        "plt.title('Top transaction codes with highest gender difference', size = 35, color='#4f4e4e')\n",
        "plt.xlabel('Gender difference coefficient')\n",
        "plt.ylabel('')\n",
        "\n",
        "labels = ['Gender 0', 'Gender 1']\n",
        "h, l = ax.get_legend_handles_labels()\n",
        "\n",
        "ax.legend(h, labels, title=\"Majority for code\", framealpha=0.0, loc=\"lower right\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "vGULY1Ugx4gw"
      },
      "outputs": [],
      "source": [
        "gender_diff_mcc = list(train_gender.sort_values('coef', ascending=False).iloc[:20]['mcc_code'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "bhMXyk_CySBa",
        "outputId": "6bf92dbb-f4bd-4a5d-e8ed-951039a9f8f1"
      },
      "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>mcc_code</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7995</td>\n",
              "      <td>12744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5533</td>\n",
              "      <td>11185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7994</td>\n",
              "      <td>5233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5621</td>\n",
              "      <td>4287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>4784</td>\n",
              "      <td>841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>6211</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>7993</td>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>5532</td>\n",
              "      <td>338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>5931</td>\n",
              "      <td>289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>5013</td>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>5967</td>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>7512</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>5965</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>5940</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>5072</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>5131</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>1731</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>3351</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>9211</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>8244</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     mcc_code  count\n",
              "19       7995  12744\n",
              "23       5533  11185\n",
              "39       7994   5233\n",
              "44       5621   4287\n",
              "81       4784    841\n",
              "85       6211    750\n",
              "99       7993    468\n",
              "102      5532    338\n",
              "107      5931    289\n",
              "113      5013    240\n",
              "115      5967    228\n",
              "141      7512    114\n",
              "143      5965    108\n",
              "144      5940    106\n",
              "156      5072     70\n",
              "162      5131     59\n",
              "168      1731     48\n",
              "173      3351     37\n",
              "174      9211     35\n",
              "180      8244     29"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# from this table we can see that mcc with high gender difference are quite rare\n",
        "\n",
        "mcc_counts = transactions['mcc_code'].value_counts().reset_index()\n",
        "mcc_counts.columns = ['mcc_code', 'count']\n",
        "\n",
        "mcc_counts[mcc_counts['mcc_code'].isin(gender_diff_mcc)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uILqLCDxLQPu",
        "outputId": "96bc2909-2052-4b34-dfb8-033faaee5c06"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>client_id</th>\n",
              "      <th>trans_time</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "      <th>amount_log</th>\n",
              "      <th>income</th>\n",
              "      <th>spent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>35 08:24:41</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-7.500839</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1808.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>81c7349f7bdcb661bc7d5f5f0fdac6aa</td>\n",
              "      <td>351 13:27:15</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>442207</td>\n",
              "      <td>Kazan</td>\n",
              "      <td>8.981620</td>\n",
              "      <td>7954.51</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fc0c7c937f139bd6d1c5b5d0a0023dd3</td>\n",
              "      <td>150 16:03:59</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-12293.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.416912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-12293.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3495487d5beaa5caaa0682fddc38379a</td>\n",
              "      <td>373 11:54:12</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>49.52</td>\n",
              "      <td>888987</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>3.922369</td>\n",
              "      <td>49.52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07e279042b1da23710fa2f65034d30b9</td>\n",
              "      <td>340 13:11:45</td>\n",
              "      <td>6010</td>\n",
              "      <td>7070</td>\n",
              "      <td>45.29</td>\n",
              "      <td>888918</td>\n",
              "      <td>Saint Petersburg</td>\n",
              "      <td>3.834926</td>\n",
              "      <td>45.29</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          client_id    trans_time  mcc_code  trans_type  \\\n",
              "0  d1bbbc9a0e0410d3cf12a3d2f44f3450   35 08:24:41      4829        2370   \n",
              "1  81c7349f7bdcb661bc7d5f5f0fdac6aa  351 13:27:15      6011        7010   \n",
              "2  fc0c7c937f139bd6d1c5b5d0a0023dd3  150 16:03:59      6011        2010   \n",
              "3  3495487d5beaa5caaa0682fddc38379a  373 11:54:12      6010        7070   \n",
              "4  07e279042b1da23710fa2f65034d30b9  340 13:11:45      6010        7070   \n",
              "\n",
              "     amount term_id        trans_city  amount_log   income     spent  \n",
              "0  -1808.56     NaN              Tver   -7.500839      NaN  -1808.56  \n",
              "1   7954.51  442207             Kazan    8.981620  7954.51       NaN  \n",
              "2 -12293.56     NaN        Khabarovsk   -9.416912      NaN -12293.56  \n",
              "3     49.52  888987       Kaliningrad    3.922369    49.52       NaN  \n",
              "4     45.29  888918  Saint Petersburg    3.834926    45.29       NaN  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transactions.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "_8_hNI2X3VLU"
      },
      "outputs": [],
      "source": [
        "trans_train = pd.merge(transactions, train, on='client_id')\n",
        "trans_test = pd.merge(transactions, test, on='client_id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Tf8c_ctV3OJ6"
      },
      "outputs": [],
      "source": [
        "base_date = pd.to_datetime('2022-01-01')\n",
        "\n",
        "for df in [trans_train, trans_test]:\n",
        "    df['trans_time'] = df['trans_time'].astype(str)\n",
        "    df[['days', 'time']] = df['trans_time'].str.split(' ', expand=True)\n",
        "    df['days'] = pd.to_timedelta(df['days'].astype(int), unit='D')\n",
        "    df['time'] = pd.to_timedelta(df['time'])\n",
        "    df['event_time'] = base_date + df['days'] + df['time']\n",
        "    df.drop(['trans_time', 'days', 'time'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ji_DG4rgMWmf",
        "outputId": "ec354ff1-543c-4539-96f5-2f657d733ad8"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "      <th>amount_log</th>\n",
              "      <th>income</th>\n",
              "      <th>spent</th>\n",
              "      <th>gender</th>\n",
              "      <th>event_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-7.500839</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1808.56</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-02-05 08:24:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-3390.41</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-8.129001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-3390.41</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-04-16 12:57:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>4814</td>\n",
              "      <td>1030</td>\n",
              "      <td>-144.50</td>\n",
              "      <td>889003</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-4.980176</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-144.50</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-04-01 19:32:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-3542.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-8.172814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-3542.30</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-03-25 09:22:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d1bbbc9a0e0410d3cf12a3d2f44f3450</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-3542.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tver</td>\n",
              "      <td>-8.172927</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-3542.70</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-03-16 13:31:57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          client_id  mcc_code  trans_type   amount term_id  \\\n",
              "0  d1bbbc9a0e0410d3cf12a3d2f44f3450      4829        2370 -1808.56     NaN   \n",
              "1  d1bbbc9a0e0410d3cf12a3d2f44f3450      4829        2370 -3390.41     NaN   \n",
              "2  d1bbbc9a0e0410d3cf12a3d2f44f3450      4814        1030  -144.50  889003   \n",
              "3  d1bbbc9a0e0410d3cf12a3d2f44f3450      6011        2010 -3542.30     NaN   \n",
              "4  d1bbbc9a0e0410d3cf12a3d2f44f3450      6011        2010 -3542.70     NaN   \n",
              "\n",
              "  trans_city  amount_log  income    spent  gender          event_time  \n",
              "0       Tver   -7.500839     NaN -1808.56       0 2022-02-05 08:24:41  \n",
              "1       Tver   -8.129001     NaN -3390.41       0 2022-04-16 12:57:32  \n",
              "2       Tver   -4.980176     NaN  -144.50       0 2023-04-01 19:32:01  \n",
              "3       Tver   -8.172814     NaN -3542.30       0 2022-03-25 09:22:26  \n",
              "4       Tver   -8.172927     NaN -3542.70       0 2022-03-16 13:31:57  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "2YfkqpnrIBi1"
      },
      "outputs": [],
      "source": [
        "# to get hour and minute\n",
        "trans_train['hour'] = trans_train['event_time'].dt.strftime('%H')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "mtK7E8XaIaVC",
        "outputId": "0fb27783-33d4-4ac0-bbd6-519b4db5e559"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "      <th>amount_log</th>\n",
              "      <th>income</th>\n",
              "      <th>spent</th>\n",
              "      <th>gender</th>\n",
              "      <th>event_time</th>\n",
              "      <th>hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3238727</th>\n",
              "      <td>b6714c0ca16193cbd419623eb70b1488</td>\n",
              "      <td>4814</td>\n",
              "      <td>1030</td>\n",
              "      <td>-73.31</td>\n",
              "      <td>941133</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-4.308246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.31</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-03-22 16:57:21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238728</th>\n",
              "      <td>ee4c5d78cbb27771979714ffa903ef5d</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>72.71</td>\n",
              "      <td>483632</td>\n",
              "      <td>Moscow</td>\n",
              "      <td>4.300138</td>\n",
              "      <td>72.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-11-10 15:12:47</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238729</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>10172441</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.579447</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-22 16:37:17</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238730</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>10172440</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.802510</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-10 16:39:52</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238731</th>\n",
              "      <td>69c0bee7e4b32fec8b3038548de2581e</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>-9.356232</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-04-07 15:11:21</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                client_id  mcc_code  trans_type    amount  \\\n",
              "3238727  b6714c0ca16193cbd419623eb70b1488      4814        1030    -73.31   \n",
              "3238728  ee4c5d78cbb27771979714ffa903ef5d      6011        7010     72.71   \n",
              "3238729  d428ad799c5790b036beb67cc1b81075      6011        2010 -14463.42   \n",
              "3238730  d428ad799c5790b036beb67cc1b81075      6011        2010 -18078.07   \n",
              "3238731  69c0bee7e4b32fec8b3038548de2581e      4829        2370 -11569.71   \n",
              "\n",
              "          term_id   trans_city  amount_log  income     spent  gender  \\\n",
              "3238727    941133   Khabarovsk   -4.308246     NaN    -73.31       1   \n",
              "3238728    483632       Moscow    4.300138   72.71       NaN       0   \n",
              "3238729  10172441   Khabarovsk   -9.579447     NaN -14463.42       0   \n",
              "3238730  10172440   Khabarovsk   -9.802510     NaN -18078.07       0   \n",
              "3238731       NaN  Kaliningrad   -9.356232     NaN -11569.71       1   \n",
              "\n",
              "                 event_time hour  \n",
              "3238727 2023-03-22 16:57:21   16  \n",
              "3238728 2022-11-10 15:12:47   15  \n",
              "3238729 2023-03-22 16:37:17   16  \n",
              "3238730 2023-03-10 16:39:52   16  \n",
              "3238731 2022-04-07 15:11:21   15  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3efFCvFWN7e",
        "outputId": "8a6a8f12-9301-411a-b5b0-cefc7aa39583"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3238732, 12)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "6D9k9DtJRY-Z"
      },
      "outputs": [],
      "source": [
        "trans_train['hour'] = trans_train['hour'].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3poMNm5WS9D",
        "outputId": "a092e96a-46f3-43cf-efed-7b2b16843577"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "24"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train['hour'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "upDirjvjRu3N",
        "outputId": "75e7a012-27c4-4fa6-9144-f035a93cb11f"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3238727</th>\n",
              "      <td>b6714c0ca16193cbd419623eb70b1488</td>\n",
              "      <td>4814</td>\n",
              "      <td>1030</td>\n",
              "      <td>-73.31</td>\n",
              "      <td>941133</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-4.308246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.31</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-03-22 16:57:21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238728</th>\n",
              "      <td>ee4c5d78cbb27771979714ffa903ef5d</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>72.71</td>\n",
              "      <td>483632</td>\n",
              "      <td>Moscow</td>\n",
              "      <td>4.300138</td>\n",
              "      <td>72.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-11-10 15:12:47</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238729</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>10172441</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.579447</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-22 16:37:17</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238730</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>10172440</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.802510</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-10 16:39:52</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238731</th>\n",
              "      <td>69c0bee7e4b32fec8b3038548de2581e</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>-9.356232</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-04-07 15:11:21</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                client_id  mcc_code  trans_type    amount  \\\n",
              "3238727  b6714c0ca16193cbd419623eb70b1488      4814        1030    -73.31   \n",
              "3238728  ee4c5d78cbb27771979714ffa903ef5d      6011        7010     72.71   \n",
              "3238729  d428ad799c5790b036beb67cc1b81075      6011        2010 -14463.42   \n",
              "3238730  d428ad799c5790b036beb67cc1b81075      6011        2010 -18078.07   \n",
              "3238731  69c0bee7e4b32fec8b3038548de2581e      4829        2370 -11569.71   \n",
              "\n",
              "          term_id   trans_city  amount_log  income     spent  gender  \\\n",
              "3238727    941133   Khabarovsk   -4.308246     NaN    -73.31       1   \n",
              "3238728    483632       Moscow    4.300138   72.71       NaN       0   \n",
              "3238729  10172441   Khabarovsk   -9.579447     NaN -14463.42       0   \n",
              "3238730  10172440   Khabarovsk   -9.802510     NaN -18078.07       0   \n",
              "3238731       NaN  Kaliningrad   -9.356232     NaN -11569.71       1   \n",
              "\n",
              "                 event_time  hour  \n",
              "3238727 2023-03-22 16:57:21    16  \n",
              "3238728 2022-11-10 15:12:47    15  \n",
              "3238729 2023-03-22 16:37:17    16  \n",
              "3238730 2023-03-10 16:39:52    16  \n",
              "3238731 2022-04-07 15:11:21    15  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [],
      "source": [
        "def part_of_day(x):\n",
        "    if x < 5 or x > 23:\n",
        "        return 'Night'\n",
        "    if x < 10:\n",
        "        return 'Morning'\n",
        "    if x < 17:\n",
        "        return 'Daytime'\n",
        "    return 'Evening'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [],
      "source": [
        "trans_train['part_of_day'] = trans_train['hour'].apply(part_of_day)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "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>client_id</th>\n",
              "      <th>mcc_code</th>\n",
              "      <th>trans_type</th>\n",
              "      <th>amount</th>\n",
              "      <th>term_id</th>\n",
              "      <th>trans_city</th>\n",
              "      <th>amount_log</th>\n",
              "      <th>income</th>\n",
              "      <th>spent</th>\n",
              "      <th>gender</th>\n",
              "      <th>event_time</th>\n",
              "      <th>hour</th>\n",
              "      <th>Night</th>\n",
              "      <th>Morning</th>\n",
              "      <th>Day</th>\n",
              "      <th>Evening</th>\n",
              "      <th>part_of_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3238727</th>\n",
              "      <td>b6714c0ca16193cbd419623eb70b1488</td>\n",
              "      <td>4814</td>\n",
              "      <td>1030</td>\n",
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              "      <td>941133</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-4.308246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.31</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-03-22 16:57:21</td>\n",
              "      <td>16</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Daytime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238728</th>\n",
              "      <td>ee4c5d78cbb27771979714ffa903ef5d</td>\n",
              "      <td>6011</td>\n",
              "      <td>7010</td>\n",
              "      <td>72.71</td>\n",
              "      <td>483632</td>\n",
              "      <td>Moscow</td>\n",
              "      <td>4.300138</td>\n",
              "      <td>72.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-11-10 15:12:47</td>\n",
              "      <td>15</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Daytime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238729</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>10172441</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.579447</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-14463.42</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-22 16:37:17</td>\n",
              "      <td>16</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Daytime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238730</th>\n",
              "      <td>d428ad799c5790b036beb67cc1b81075</td>\n",
              "      <td>6011</td>\n",
              "      <td>2010</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>10172440</td>\n",
              "      <td>Khabarovsk</td>\n",
              "      <td>-9.802510</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-18078.07</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-03-10 16:39:52</td>\n",
              "      <td>16</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Daytime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238731</th>\n",
              "      <td>69c0bee7e4b32fec8b3038548de2581e</td>\n",
              "      <td>4829</td>\n",
              "      <td>2370</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kaliningrad</td>\n",
              "      <td>-9.356232</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-11569.71</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-04-07 15:11:21</td>\n",
              "      <td>15</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Daytime</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                client_id  mcc_code  trans_type    amount  \\\n",
              "3238727  b6714c0ca16193cbd419623eb70b1488      4814        1030    -73.31   \n",
              "3238728  ee4c5d78cbb27771979714ffa903ef5d      6011        7010     72.71   \n",
              "3238729  d428ad799c5790b036beb67cc1b81075      6011        2010 -14463.42   \n",
              "3238730  d428ad799c5790b036beb67cc1b81075      6011        2010 -18078.07   \n",
              "3238731  69c0bee7e4b32fec8b3038548de2581e      4829        2370 -11569.71   \n",
              "\n",
              "          term_id   trans_city  amount_log  income     spent  gender  \\\n",
              "3238727    941133   Khabarovsk   -4.308246     NaN    -73.31       1   \n",
              "3238728    483632       Moscow    4.300138   72.71       NaN       0   \n",
              "3238729  10172441   Khabarovsk   -9.579447     NaN -14463.42       0   \n",
              "3238730  10172440   Khabarovsk   -9.802510     NaN -18078.07       0   \n",
              "3238731       NaN  Kaliningrad   -9.356232     NaN -11569.71       1   \n",
              "\n",
              "                 event_time  hour  Night  Morning   Day  Evening part_of_day  \n",
              "3238727 2023-03-22 16:57:21    16  False    False  True    False     Daytime  \n",
              "3238728 2022-11-10 15:12:47    15  False    False  True    False     Daytime  \n",
              "3238729 2023-03-22 16:37:17    16  False    False  True    False     Daytime  \n",
              "3238730 2023-03-10 16:39:52    16  False    False  True    False     Daytime  \n",
              "3238731 2022-04-07 15:11:21    15  False    False  True    False     Daytime  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trans_train.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Daytime    1414945\n",
              "Evening     989532\n",
              "Morning     510971\n",
              "Night       323284\n",
              "Name: part_of_day, dtype: int64"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pod_counts = trans_train['part_of_day'].value_counts()\n",
        "pod_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(15, 10))\n",
        "\n",
        "ax.pie(pod_counts, labels=pod_counts.index, autopct='%1.1f%%');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "vnqIY0NZ4vmg",
        "outputId": "10067142-6979-4d02-8a47-e9b24607dcdc"
      },
      "outputs": [],
      "source": [
        "# rs = np.random.RandomState(42)\n",
        "# corr = trans_train.corr()\n",
        "# corr.style.background_gradient(cmap='coolwarm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "_hneXAbUflgq",
        "outputId": "876ea515-aad6-4bb1-ee9e-ac63f72d30b0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trans_train['gender'].value_counts(dropna = False).plot.bar(color='#182c6bff', figsize=(20, 10))\n",
        "\n",
        "plt.title('Distribution of gender (transactions)')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trans_train.groupby('client_id')['gender'].agg('first').value_counts().plot.bar(color='#d2de26ff', figsize=(20, 10))\n",
        "\n",
        "plt.title('Distribution of gender (clients)')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'log(length)')"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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jgT0AAAAAAAAAFEBgDwAAAAAAAAAFENgDAAAAAAAAQAEE9gAAAAAAAABQAIE9AAAAAAAAABRAYA8AAAAAAAAABRDYAwAAAAAAAEABBPYAAAAAAAAAUID/H1ElUR494jT2AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trans_history_len = trans_train.groupby('client_id')['mcc_code'].apply(len)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(25, 10))\n",
        "\n",
        "ax.hist(np.log(1 + trans_history_len), bins=50, color='#182c6bff')\n",
        "ax.set_title('Distribution of history length')\n",
        "ax.set_xlabel('log(length)')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "env",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
    },
    "vscode": {
      "interpreter": {
        "hash": "1d85f8d091281e8cdbbabc30d35abdaebd4eb091dd84d434d9fa6db6a3942f76"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}