ARL_Banking_Product_Recommender.ipynb

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      "name": "ARL_Banking_Product_Recommender.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyOJYZj7C4OTKKZGi/qc/MzH",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chiroiu96/ARL_Banking_Product_Recommender/blob/main/ARL_Banking_Product_Recommender.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNAAdwku1nPx"
      },
      "source": [
        "#Banking Product Recommendation\n",
        "[Kaggle Santander Dataset](https://www.kaggle.com/c/santander-product-recommendation/data)  with 1.5 years of customers behavior data from Santander bank.\n",
        "\n",
        "The goal is to predict what new products customers will purchase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBMfYPPFxKYd"
      },
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tm2Jbf2xxMah"
      },
      "source": [
        "#Read Ratings DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "nwIFTo5ho6zP",
        "outputId": "2e9da86d-fdb9-4c3c-c780-8502b7617aa1"
      },
      "source": [
        "df1 = pd.read_csv('https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/train_ARL_1.csv', sep=',')\n",
        "df2 = pd.read_csv('https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/train_ARL_2.csv', sep=',')\n",
        "df = df1.append(df2, ignore_index=True)\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>SAVING_ACC</th>\n",
              "      <th>GUARANTEES</th>\n",
              "      <th>CURRENT_ACC</th>\n",
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              "      <th>PAYROLL_ACC</th>\n",
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              "</div>"
            ],
            "text/plain": [
              "   SAVING_ACC  GUARANTEES  CURRENT_ACC  ...  HOME_ACC  PAYROLL  DIRECT_DEBIT\n",
              "0           0           0           17  ...         0        0             0\n",
              "1           0           0            0  ...         0       17            17\n",
              "2           0           0            1  ...         0        0             0\n",
              "3           0           0           12  ...         0        0            17\n",
              "4           0           0            0  ...         0        0             0\n",
              "5           0           0           17  ...         0       17            17\n",
              "6           0           0           17  ...         0        0            17\n",
              "7           0           0            8  ...         0        0             0\n",
              "8           0           0           14  ...         0        0            17\n",
              "9           0           0            0  ...         0        0             0\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEhijC9hxo2P"
      },
      "source": [
        "#Binary Encoding of dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rr1K_VRopwe3"
      },
      "source": [
        "def encode_units(x):\n",
        "    if x <= 0:\n",
        "        return 0\n",
        "    if x >= 1:\n",
        "        return 1\n",
        "df = df.applymap(encode_units)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-wAoHjWxyE5"
      },
      "source": [
        "#Binary dataframe sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Dx1CwkU7x05w",
        "outputId": "1420659f-d76d-47bc-8e18-66d413d42632"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SAVING_ACC  GUARANTEES  CURRENT_ACC  ...  HOME_ACC  PAYROLL  DIRECT_DEBIT\n",
              "0           0           0            1  ...         0        0             0\n",
              "1           0           0            0  ...         0        1             1\n",
              "2           0           0            1  ...         0        0             0\n",
              "3           0           0            1  ...         0        0             1\n",
              "4           0           0            0  ...         0        0             0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHM9DgCNzcm-"
      },
      "source": [
        "#Support, Confidence, Lift"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fvWuRTyrWhn"
      },
      "source": [
        "\n",
        "  <tr>\n",
        "    <td rowspan=\"3\"><img src=\"https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/Apriori_Transactions.JPG\" ></td>\n",
        "    <td>\n",
        "      <img src=\"https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/Apriori_Support.JPG\" >\n",
        "    </td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>\n",
        "      <img src=\"https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/Apriori_Confidence.JPG\" >\n",
        "    </td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>\n",
        "      <img src=\"https://raw.githubusercontent.com/chiroiu96/ARL_Banking_Product_Recommender/main/Apriori_Lift.JPG\" >\n",
        "    </td>\n",
        "  </tr>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwE-wotox40c"
      },
      "source": [
        "#Generation of most frequent items"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN8yHzsYx6kw"
      },
      "source": [
        "frequent_itemsets = apriori(df, min_support=0.05, use_colnames=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3zkUlF3x8sS"
      },
      "source": [
        "#Generation of association rules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "nXzS9f-pyBm8",
        "outputId": "c15cd1f2-0791-481b-9855-a4a27ee9e1e8"
      },
      "source": [
        "rules = association_rules(frequent_itemsets, metric='lift', min_threshold=1)\n",
        "rules.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(PARTICULAR_ACC)</td>\n",
              "      <td>(CURRENT_ACC)</td>\n",
              "      <td>0.113003</td>\n",
              "      <td>0.650577</td>\n",
              "      <td>0.078326</td>\n",
              "      <td>0.693129</td>\n",
              "      <td>1.065407</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>1.138664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(CURRENT_ACC)</td>\n",
              "      <td>(PARTICULAR_ACC)</td>\n",
              "      <td>0.650577</td>\n",
              "      <td>0.113003</td>\n",
              "      <td>0.078326</td>\n",
              "      <td>0.120394</td>\n",
              "      <td>1.065407</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>1.008403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(E_ACC)</td>\n",
              "      <td>(CURRENT_ACC)</td>\n",
              "      <td>0.085633</td>\n",
              "      <td>0.650577</td>\n",
              "      <td>0.055844</td>\n",
              "      <td>0.652136</td>\n",
              "      <td>1.002397</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>1.004483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(CURRENT_ACC)</td>\n",
              "      <td>(E_ACC)</td>\n",
              "      <td>0.650577</td>\n",
              "      <td>0.085633</td>\n",
              "      <td>0.055844</td>\n",
              "      <td>0.085838</td>\n",
              "      <td>1.002397</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>1.000225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(CURRENT_ACC)</td>\n",
              "      <td>(DIRECT_DEBIT)</td>\n",
              "      <td>0.650577</td>\n",
              "      <td>0.164312</td>\n",
              "      <td>0.121735</td>\n",
              "      <td>0.187118</td>\n",
              "      <td>1.138801</td>\n",
              "      <td>0.014837</td>\n",
              "      <td>1.028056</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        antecedents       consequents  ...  leverage  conviction\n",
              "0  (PARTICULAR_ACC)     (CURRENT_ACC)  ...  0.004809    1.138664\n",
              "1     (CURRENT_ACC)  (PARTICULAR_ACC)  ...  0.004809    1.008403\n",
              "2           (E_ACC)     (CURRENT_ACC)  ...  0.000134    1.004483\n",
              "3     (CURRENT_ACC)           (E_ACC)  ...  0.000134    1.000225\n",
              "4     (CURRENT_ACC)    (DIRECT_DEBIT)  ...  0.014837    1.028056\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9JzfvyRyEhd"
      },
      "source": [
        "#Choosing the rules with high impact\n",
        "We are interested in rules:\n",
        "\n",
        "\n",
        "*   Have a support as hight as possible (value depende on number of items)\n",
        "*   Have a confidence close to 1\n",
        "*   Have a lift higher then 1 (positive correlation)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "xaSPH2YxyHxD",
        "outputId": "1f5c129d-7411-40be-9ccb-f70c1eeba4e8"
      },
      "source": [
        "rules[ (rules['lift'] >= 6) &\n",
        "      (rules['confidence'] >= 0.8) ]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(PAYROLL)</td>\n",
              "      <td>(PAYROLL_ACC)</td>\n",
              "      <td>0.073545</td>\n",
              "      <td>0.089123</td>\n",
              "      <td>0.070949</td>\n",
              "      <td>0.964708</td>\n",
              "      <td>10.824466</td>\n",
              "      <td>0.064394</td>\n",
              "      <td>25.809773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(PAYROLL)</td>\n",
              "      <td>(DIRECT_DEBIT)</td>\n",
              "      <td>0.073545</td>\n",
              "      <td>0.164312</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.819234</td>\n",
              "      <td>4.985850</td>\n",
              "      <td>0.048166</td>\n",
              "      <td>4.623029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(PAYROLL, PAYROLL_ACC)</td>\n",
              "      <td>(DIRECT_DEBIT)</td>\n",
              "      <td>0.070949</td>\n",
              "      <td>0.164312</td>\n",
              "      <td>0.058630</td>\n",
              "      <td>0.826367</td>\n",
              "      <td>5.029263</td>\n",
              "      <td>0.046972</td>\n",
              "      <td>4.812955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(PAYROLL, DIRECT_DEBIT)</td>\n",
              "      <td>(PAYROLL_ACC)</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.089123</td>\n",
              "      <td>0.058630</td>\n",
              "      <td>0.973108</td>\n",
              "      <td>10.918717</td>\n",
              "      <td>0.053260</td>\n",
              "      <td>33.871699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(DIRECT_DEBIT, PAYROLL_ACC)</td>\n",
              "      <td>(PAYROLL)</td>\n",
              "      <td>0.071219</td>\n",
              "      <td>0.073545</td>\n",
              "      <td>0.058630</td>\n",
              "      <td>0.823238</td>\n",
              "      <td>11.193731</td>\n",
              "      <td>0.053392</td>\n",
              "      <td>5.241247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    antecedents     consequents  ...  leverage  conviction\n",
              "6                     (PAYROLL)   (PAYROLL_ACC)  ...  0.064394   25.809773\n",
              "10                    (PAYROLL)  (DIRECT_DEBIT)  ...  0.048166    4.623029\n",
              "12       (PAYROLL, PAYROLL_ACC)  (DIRECT_DEBIT)  ...  0.046972    4.812955\n",
              "13      (PAYROLL, DIRECT_DEBIT)   (PAYROLL_ACC)  ...  0.053260   33.871699\n",
              "14  (DIRECT_DEBIT, PAYROLL_ACC)       (PAYROLL)  ...  0.053392    5.241247\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}