digikala_sales.ipynb

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  "metadata": {
    "colab": {
      "name": "digikala_sales.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNYf+T/SvgXtBxSfRNOmYl4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amirhoseinRj/Digikala-sales-prediction/blob/master/digikala_sales.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOOz8Jk2FF7N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "outputId": "732ffdea-6971-4f2e-eca2-2a2b17191f1d"
      },
      "source": [
        "pip install fastai --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fastai\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/cf/9140964d3544d904cc718e519e1632a8a42e1eaaf2fafdaaa14716c42336/fastai-2.0.10-py3-none-any.whl (354kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from fastai) (3.2.2)\n",
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            "Collecting fastcore>=1.0.5\n",
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            "Installing collected packages: fastcore, fastai\n",
            "  Found existing installation: fastai 1.0.61\n",
            "    Uninstalling fastai-1.0.61:\n",
            "      Successfully uninstalled fastai-1.0.61\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXm5WhsPFXpF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.tabular.all import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DED4id-qFdGZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFWyWd-7GSJ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "042f35e6-a6a5-4c41-c2ee-595694b622ba"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "root_dir = \"/content/gdrive/My Drive/\"\n",
        "base_dir = root_dir + 'digi_sale/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGSHxDcrGY8F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "923dcc95-6cdf-4b39-d5de-05e174053759"
      },
      "source": [
        "train_df = pd.read_csv('/content/gdrive/My Drive/digikala sales prediction/train.csv')\n",
        "train_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>seller</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0</td>\n",
              "      <td>2015-05-01</td>\n",
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              "      <td>1</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2015-05-01</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2015-05-01</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2015-05-01</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
              "      <td>2015-05-01</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id        date  seller  item  sales\n",
              "0   0  2015-05-01       1     1     21\n",
              "1   1  2015-05-01       2     1     45\n",
              "2   2  2015-05-01       3     1     28\n",
              "3   3  2015-05-01       4     1     34\n",
              "4   4  2015-05-01       5     1     21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-wFHfJlHN67",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "accbb394-2471-498e-c10b-a9d9025ac61c"
      },
      "source": [
        "test_df = pd.read_csv('/content/gdrive/My Drive/digikala sales prediction/test.csv')\n",
        "test_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>seller</th>\n",
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              "      <td>2020-04-04</td>\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-04-04</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "   id        date  seller  item\n",
              "0   0  2020-04-04       1     1\n",
              "1   1  2020-04-04       2     1\n",
              "2   2  2020-04-04       3     1\n",
              "3   3  2020-04-04       4     1\n",
              "4   4  2020-04-04       5     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtzIDdw2Une8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.tabular.data import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNneEWcJHWOL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = add_datepart(train_df, \"date\")\n",
        "test = add_datepart(test_df, \"date\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rg-xkUoH09T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8844faf0-51a4-483c-f14f-104fd5e5bfb5"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>seller</th>\n",
              "      <th>Week</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Dayofweek</th>\n",
              "      <th>Dayofyear</th>\n",
              "      <th>Is_month_end</th>\n",
              "      <th>Is_month_start</th>\n",
              "      <th>Is_quarter_end</th>\n",
              "      <th>Is_quarter_start</th>\n",
              "      <th>Is_year_end</th>\n",
              "      <th>Is_year_start</th>\n",
              "      <th>Elapsed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>121</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1430438400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>121</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1430438400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>121</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1430438400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>121</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1430438400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>121</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1430438400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  seller  Week  ...  Is_year_end  Is_year_start     Elapsed\n",
              "0   0       1    18  ...        False          False  1430438400\n",
              "1   1       2    18  ...        False          False  1430438400\n",
              "2   2       3    18  ...        False          False  1430438400\n",
              "3   3       4    18  ...        False          False  1430438400\n",
              "4   4       5    18  ...        False          False  1430438400\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSFh0wwFKfNi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "procs=[FillMissing, Categorify, Normalize]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aje-6FMRKuqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_vars = ['seller', 'Dayofweek', 'Year', 'Month', 'Day', 'item']\n",
        "cont_vars = []\n",
        "dep_var = 'sales'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ek5Dds1fLO7J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = TabularDataLoaders.from_df(train, path='.', cat_names=cat_vars, cont_names=cont_vars, procs=procs, valid_idx = list(range(800000,900000)) ,y_names = dep_var , bs = 128)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZ1TMyizMdQx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = tabular_learner(data, layers=[1000,500], ps=[0.001,0.01], emb_drop=0.04, metrics=accuracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIgkd5q8YdrV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn2 = tabular_learner(data, layers=[1000,500], ps=[0.001,0.01], emb_drop=0.06, metrics=accuracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjWIv1ncPQ5J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "23dea263-d6b0-4bd1-8008-18b8227fe0ed"
      },
      "source": [
        "learn.model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TabularModel(\n",
              "  (embeds): ModuleList(\n",
              "    (0): Embedding(11, 6)\n",
              "    (1): Embedding(8, 5)\n",
              "    (2): Embedding(7, 5)\n",
              "    (3): Embedding(13, 7)\n",
              "    (4): Embedding(32, 11)\n",
              "    (5): Embedding(46, 14)\n",
              "  )\n",
              "  (emb_drop): Dropout(p=0.0, inplace=False)\n",
              "  (bn_cont): BatchNorm1d(0, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (layers): Sequential(\n",
              "    (0): LinBnDrop(\n",
              "      (0): BatchNorm1d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Linear(in_features=48, out_features=1000, bias=False)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (1): LinBnDrop(\n",
              "      (0): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (1): Linear(in_features=1000, out_features=500, bias=False)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): LinBnDrop(\n",
              "      (0): Linear(in_features=500, out_features=1, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYPj_5APQRv5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "212b21e4-332a-41aa-d2eb-7ad2ae5f28cc"
      },
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.010000000149011612, lr_steep=0.00363078061491251)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IayV0R3LQWaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "13bc0c9a-e48d-4fd1-b254-539919ab0d17"
      },
      "source": [
        "learn.fit_one_cycle(5, 3e-2, wd=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>93.351868</td>\n",
              "      <td>531.001892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>89.326080</td>\n",
              "      <td>530.611267</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>80.982368</td>\n",
              "      <td>519.837280</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>66.508873</td>\n",
              "      <td>515.235718</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>57.657768</td>\n",
              "      <td>694.010498</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HapGWZ8cal_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dl = learn.dls.test_dl(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCFUyV4BAty0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b161be89-ca70-4f26-ca64-bb55e21af2e9"
      },
      "source": [
        "x = learn.get_preds(dl=dl)\n",
        "a = list()\n",
        "for i in range(len(x[0])):\n",
        "  a.append(x[0][i].item())\n",
        "test['sales'] = a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziirtIEKRj7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = test.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoQWxwsMBdBo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c50495c9-bb77-44b7-fe75-4795dea36ff7"
      },
      "source": [
        "prediction"
      ],
      "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>id</th>\n",
              "      <th>seller</th>\n",
              "      <th>Week</th>\n",
              "      <th>item</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Dayofweek</th>\n",
              "      <th>Dayofyear</th>\n",
              "      <th>Is_month_end</th>\n",
              "      <th>Is_month_start</th>\n",
              "      <th>Is_quarter_end</th>\n",
              "      <th>Is_quarter_start</th>\n",
              "      <th>Is_year_end</th>\n",
              "      <th>Is_year_start</th>\n",
              "      <th>Elapsed</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1585958400</td>\n",
              "      <td>38.009098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1585958400</td>\n",
              "      <td>51.927322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1585958400</td>\n",
              "      <td>46.325024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1585958400</td>\n",
              "      <td>42.813789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1585958400</td>\n",
              "      <td>30.965132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12995</th>\n",
              "      <td>12995</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1588118400</td>\n",
              "      <td>25.768808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12996</th>\n",
              "      <td>12996</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1588118400</td>\n",
              "      <td>22.596062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12997</th>\n",
              "      <td>12997</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1588118400</td>\n",
              "      <td>41.775902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12998</th>\n",
              "      <td>12998</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1588118400</td>\n",
              "      <td>37.117458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12999</th>\n",
              "      <td>12999</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>50</td>\n",
              "      <td>2020</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1588118400</td>\n",
              "      <td>39.002350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13000 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  seller  Week  ...  Is_year_start     Elapsed      sales\n",
              "0          0       1    14  ...          False  1585958400  38.009098\n",
              "1          1       2    14  ...          False  1585958400  51.927322\n",
              "2          2       3    14  ...          False  1585958400  46.325024\n",
              "3          3       4    14  ...          False  1585958400  42.813789\n",
              "4          4       5    14  ...          False  1585958400  30.965132\n",
              "...      ...     ...   ...  ...            ...         ...        ...\n",
              "12995  12995       6    18  ...          False  1588118400  25.768808\n",
              "12996  12996       7    18  ...          False  1588118400  22.596062\n",
              "12997  12997       8    18  ...          False  1588118400  41.775902\n",
              "12998  12998       9    18  ...          False  1588118400  37.117458\n",
              "12999  12999      10    18  ...          False  1588118400  39.002350\n",
              "\n",
              "[13000 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oy3vyrcGBg23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction.drop(labels = ['seller', 'Week', 'item', 'Year', 'Month', 'Day' , 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start', 'Elapsed']\n",
        "                ,axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1dS0eGlCSvv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction.to_csv('/content/gdrive/My Drive/digikala sales prediction/predictions.csv' , index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cKt5jsdYlKD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1d7a9afa-6588-4390-d0f2-32873a22cb68"
      },
      "source": [
        "learn2.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.00831763744354248, lr_steep=0.00363078061491251)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "665pGoqgYuj9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "5b0bcdf8-5e80-4532-df7f-8891ca6c6bba"
      },
      "source": [
        "learn.fit_one_cycle(10, 1e-2, wd=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>65.925964</td>\n",
              "      <td>623.934387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>68.783699</td>\n",
              "      <td>511.358093</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>74.503502</td>\n",
              "      <td>504.900635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>74.394638</td>\n",
              "      <td>531.495056</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>72.344109</td>\n",
              "      <td>496.030884</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>69.686508</td>\n",
              "      <td>500.709229</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>67.917152</td>\n",
              "      <td>602.465332</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>61.124058</td>\n",
              "      <td>641.354858</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>58.406952</td>\n",
              "      <td>800.096863</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>57.596062</td>\n",
              "      <td>699.003174</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>01:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxFj3gPeT4qX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9be19018-9f83-4232-c48b-50e9c1ad8152"
      },
      "source": [
        "dl2 = learn.dls.test_dl(test)\n",
        "x2 = learn2.get_preds(dl=dl2)\n",
        "a2 = list()\n",
        "for i in range(len(x2[0])):\n",
        "  a2.append(x2[0][i].item())\n",
        "test['sales'] = a2\n",
        "prediction2 = test.copy()\n",
        "prediction2.drop(labels = ['seller', 'Week', 'item', 'Year', 'Month', 'Day' , 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start', 'Elapsed']\n",
        "                ,axis=1, inplace=True)\n",
        "prediction2.to_csv('/content/gdrive/My Drive/digikala sales prediction/predictions2.csv' , index = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIstcJ3kGmqz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}