Recommendation_System.ipynb

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      "name": "Recommendation-System.ipynb",
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      "mount_file_id": "1rgANQ38wFNDkKshzIk0dndPDJMQOwDfs",
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/PRAN20/Recommendation-System/blob/main/Recommendation_System.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Header Files**"
      ],
      "metadata": {
        "id": "edGuRQsFgVXD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LAYQM7AMezVl"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "from gensim.models import Word2Vec \n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings;\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing DataSet**"
      ],
      "metadata": {
        "id": "YMCRAN_8gal6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/Online Retail.xlsx')\n",
        "#/content/drive/MyDrive/Online Retail.xlsx"
      ],
      "metadata": {
        "id": "hIE_HcDLfxSy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Checking Dataset**"
      ],
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        "id": "OYmAcQPFgeFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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        "id": "HcnLKqMSgT3m",
        "outputId": "1b07f01d-b5c2-4db5-9250-607ccb8fb55c"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
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        "df.shape"
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        "outputId": "3553bbe2-a990-4359-8c7d-7f9661be5e53"
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      "outputs": [
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            "text/plain": [
              "(541909, 8)"
            ]
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "markdown",
      "source": [
        "# **Removing Missing Values**"
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      }
    },
    {
      "cell_type": "code",
      "source": [
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        "outputId": "78cc0ec9-10ff-492c-9e49-4817e481719f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo           0\n",
              "StockCode           0\n",
              "Description      1454\n",
              "Quantity            0\n",
              "InvoiceDate         0\n",
              "UnitPrice           0\n",
              "CustomerID     135080\n",
              "Country             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
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      "metadata": {
        "id": "ppm5mCa-gveo"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
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        "colab": {
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        "id": "lrW-enfbgzTA",
        "outputId": "0f2d3b45-1028-4be5-ddb9-0fd4371296ce"
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      "outputs": [
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          "data": {
            "text/plain": [
              "InvoiceNo      0\n",
              "StockCode      0\n",
              "Description    0\n",
              "Quantity       0\n",
              "InvoiceDate    0\n",
              "UnitPrice      0\n",
              "CustomerID     0\n",
              "Country        0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 7
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    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preparation**"
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        "id": "Z8jjL3A8hYrq"
      }
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      "cell_type": "code",
      "source": [
        "df['StockCode']= df['StockCode'].astype(str)"
      ],
      "metadata": {
        "id": "FzuVyqRYhbEW"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a546c657-b703-470c-bbe8-e4426e63b28a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a546c657-b703-470c-bbe8-e4426e63b28a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customers = df[\"CustomerID\"].unique().tolist()   #Unique Customers\n",
        "len(customers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8h6oMKsihqmh",
        "outputId": "25164240-7ce8-436f-d607-3391423e1e38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4372"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random.shuffle(customers)"
      ],
      "metadata": {
        "id": "msV7cir6h2cU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customers_train = [customers[i] for i in range(round(0.9*len(customers)))]"
      ],
      "metadata": {
        "id": "DdHhXvj7h5lJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = df[df['CustomerID'].isin(customers_train)]\n",
        "validation_df = df[~df['CustomerID'].isin(customers_train)]"
      ],
      "metadata": {
        "id": "pr4UtOofh9EW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Storing Total Purchases in a List**"
      ],
      "metadata": {
        "id": "W9Py6iHdiHKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "purchases_train = []\n",
        "\n",
        "for i in tqdm(customers_train):\n",
        "    temp = train_df[train_df[\"CustomerID\"] == i][\"StockCode\"].tolist()\n",
        "    purchases_train.append(temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFeTcKpqiGoX",
        "outputId": "d9bd3e7d-8504-4b22-d9c6-61e55e5fa814"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3935/3935 [00:03<00:00, 1005.91it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "purchases_val = []\n",
        "\n",
        "for i in tqdm(validation_df['CustomerID'].unique()):\n",
        "    temp = validation_df[validation_df[\"CustomerID\"] == i][\"StockCode\"].tolist()\n",
        "    purchases_val.append(temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CROWZ11siZlV",
        "outputId": "471bda6b-4990-49ca-ee4b-acb3af0c5fbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 437/437 [00:00<00:00, 1531.91it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Word2vec Model**"
      ],
      "metadata": {
        "id": "wY5kWpt8ihgr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Word2Vec(window = 10, sg = 1, hs = 0,\n",
        "                 negative = 10, # for negative sampling\n",
        "                 alpha=0.03, min_alpha=0.0007,\n",
        "                 seed = 14)\n",
        "\n",
        "model.build_vocab(purchases_train, progress_per=200)\n",
        "\n",
        "model.train(purchases_train, total_examples = model.corpus_count, \n",
        "            epochs=10, report_delay=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2H8co6_ilK7",
        "outputId": "fce2fab6-c440-4fc9-ab6a-cba1ce8ae8e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3648739, 3684830)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"/content/Output/word2vec_2.model\") #Most Important"
      ],
      "metadata": {
        "id": "9Zj9CsiYir6n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.init_sims(replace=True)"
      ],
      "metadata": {
        "id": "YWGDCLmAjCYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3K_cumHMjFfc",
        "outputId": "791d2642-bf78-4d08-d441-547b13d3713d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word2Vec(vocab=3175, size=100, alpha=0.03)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Constructing UMAP of the Model**"
      ],
      "metadata": {
        "id": "_y4cQCsqjKr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall umap\n",
        "!pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvJu-tyFjQq5",
        "outputId": "48f18aac-83d2-4dd2-fe76-a7af2c7101a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping umap as it is not installed.\u001b[0m\n",
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.7/dist-packages (0.5.3)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.5.7)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.21.6)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from umap-learn) (4.64.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.34.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pynndescent>=0.5->umap-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = model[model.wv.vocab]\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUVQINFsm51q",
        "outputId": "523d4239-d0d5-43db-e06d-3c94ea354e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3175, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "\n",
        "cluster_embedding = umap.UMAP(n_neighbors=30, min_dist=0.0,\n",
        "                              n_components=2, random_state=42).fit_transform(X)\n",
        "\n",
        "plt.figure(figsize=(10,9))\n",
        "plt.scatter(cluster_embedding[:, 0], cluster_embedding[:, 1], s=3, cmap='Spectral')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "B6Wk-1PAjO2z",
        "outputId": "9ca46763-988c-4e8f-89ba-d592fa596790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f92ac3c45d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Recomedation Phase**"
      ],
      "metadata": {
        "id": "UPHkztwmm_6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products = train_df[[\"StockCode\", \"Description\"]]\n",
        "products.drop_duplicates(inplace=True, subset='StockCode', keep=\"last\")\n",
        "products_dict = products.groupby('StockCode')['Description'].apply(list).to_dict()"
      ],
      "metadata": {
        "id": "GbT-bNhpnENa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "products_dict['84029E']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poDXe8WgnM-p",
        "outputId": "741467f1-b870-4182-feda-ea5b03550ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['RED WOOLLY HOTTIE WHITE HEART.']"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def similar_products(v, n = 6):\n",
        "    \n",
        "    ms = model.similar_by_vector(v, topn= n+1)[1:]\n",
        "    \n",
        "    new_ms = []\n",
        "    for j in ms:\n",
        "        pair = (products_dict[j[0]][0], j[1])\n",
        "        new_ms.append(pair)\n",
        "        \n",
        "    return new_ms  "
      ],
      "metadata": {
        "id": "Vv9TZAjxnSdf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similar_products(model['90019A'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5yM0N1znWPf",
        "outputId": "99a9d19b-a9ae-461f-9aa5-4781575b7e1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('SILVER M.O.P ORBIT DROP EARRINGS', 0.7877718210220337),\n",
              " ('WHITE VINT ART DECO CRYSTAL NECKLAC', 0.78275066614151),\n",
              " ('AMBER DROP EARRINGS W LONG BEADS', 0.7807952165603638),\n",
              " ('PINK BOUDICCA LARGE BRACELET', 0.7596126794815063),\n",
              " ('ANT COPPER RED BOUDICCA BRACELET', 0.7594028115272522),\n",
              " ('GOLD/M.O.P PENDANT ORBIT NECKLACE', 0.7572386860847473)]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def aggregate_vectors(products):\n",
        "    product_vec = []\n",
        "    for i in products:\n",
        "        try:\n",
        "            product_vec.append(model[i])\n",
        "        except KeyError:\n",
        "            continue\n",
        "        \n",
        "    return np.mean(product_vec, axis=0)"
      ],
      "metadata": {
        "id": "6nkXLEp4nZ6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aggregate_vectors(purchases_val[0]).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--XcHkD4ngro",
        "outputId": "4aa6f3fe-d697-40de-9c0d-c41f4ed0169e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100,)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar_products(aggregate_vectors(purchases_val[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvMZZ8mtni5J",
        "outputId": "6631afce-16a7-4c68-d066-701b160f2edf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('JUMBO BAG ALPHABET', 0.8001401424407959),\n",
              " ('LUNCH BAG ALPHABET DESIGN', 0.7942807674407959),\n",
              " ('JUMBO BAG VINTAGE LEAF', 0.7738019227981567),\n",
              " ('JUMBO BAG APPLES', 0.7732822895050049),\n",
              " ('JUMBO BAG SPACEBOY DESIGN', 0.7651514410972595),\n",
              " ('JUMBO BAG PEARS', 0.7638946771621704)]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar_products(aggregate_vectors(purchases_val[0][-10:]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-UWbvvsni6G",
        "outputId": "03ab4d20-0db6-4136-d3fe-c94490536b01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('JUMBO BAG VINTAGE CHRISTMAS ', 0.7463159561157227),\n",
              " (\"JUMBO BAG 50'S CHRISTMAS \", 0.7412354946136475),\n",
              " ('VINTAGE DOILY JUMBO BAG RED ', 0.6707476377487183),\n",
              " ('JUMBO BAG VINTAGE LEAF', 0.6696573495864868),\n",
              " ('JUMBO BAG PAISLEY PARK', 0.6615587472915649),\n",
              " ('JINGLE BELL HEART DECORATION', 0.6535539031028748)]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Datset - 1GB\n",
        "#IEE Kaggle \n",
        "-SQL, PIG - 80\n",
        "\n",
        "Model Save\n",
        "\n",
        "Backend - "
      ],
      "metadata": {
        "id": "VT2L5zvEEr02"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}