retail_price_optimization.ipynb

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      "source": [
        "import gc\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.sparse import csr_matrix, hstack\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import lightgbm as lgb"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "dfdIFNjIk1N1"
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      "source": [
        "data=pd.read_csv(\"train1.csv\")"
      ]
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              "   Unnamed: 0  train_id                                 name  \\\n",
              "0           0         0  MLB Cincinnati Reds T Shirt Size XL   \n",
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        "msk = np.random.rand(len(data)) < 0.8\n",
        "train = data[msk]\n",
        "test = data[~msk]"
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              "   Unnamed: 0  train_id                                 name  \\\n",
              "0           0         0  MLB Cincinnati Reds T Shirt Size XL   \n",
              "1           1         1     Razer BlackWidow Chroma Keyboard   \n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-68d776a3-48d6-48c0-8677-c196a378b703 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-68d776a3-48d6-48c0-8677-c196a378b703');\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
        }
      ],
      "source": [
        "train.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFttzBJRk1OE",
        "outputId": "55ea20dd-3f0d-4365-a25f-8656ef6754f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 8008 entries, 0 to 10000\n",
            "Data columns (total 9 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Unnamed: 0         8008 non-null   int64  \n",
            " 1   train_id           8008 non-null   int64  \n",
            " 2   name               8008 non-null   object \n",
            " 3   item_condition_id  8008 non-null   int64  \n",
            " 4   category_name      7967 non-null   object \n",
            " 5   brand_name         4578 non-null   object \n",
            " 6   price              8008 non-null   float64\n",
            " 7   shipping           8008 non-null   int64  \n",
            " 8   item_description   8008 non-null   object \n",
            "dtypes: float64(1), int64(4), object(4)\n",
            "memory usage: 625.6+ KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6G82dPKk1OF",
        "outputId": "3a9b6a44-5ef2-4cd6-f34d-3c066bf225b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    8008.000000\n",
              "mean       26.442682\n",
              "std        39.866975\n",
              "min         0.000000\n",
              "25%        10.000000\n",
              "50%        16.500000\n",
              "75%        29.000000\n",
              "max      1506.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "train.price.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3j49_Ru5k1OK",
        "outputId": "23d4f5db-b1c4-4cf5-d30a-28b316967cf1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Price Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.subplot(1, 2, 1)\n",
        "(train['price']).plot.hist(bins=50, figsize=(12, 6), edgecolor = 'white', range = [0, 250])\n",
        "plt.xlabel('price', fontsize=12)\n",
        "plt.title('Price Distribution', fontsize=12)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "np.log(train['price']+1).plot.hist(bins=50, figsize=(12,6), edgecolor='white')\n",
        "plt.xlabel('log(price+1)', fontsize=12)\n",
        "plt.title('Price Distribution', fontsize=12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvz1HDTsk1OM"
      },
      "source": [
        "- The price of items are left skewed, vast majority of items priced at 10-20. However, the most expensive item priced at 2009. So we make log-transformation on the price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvXGLV1Yk1OQ",
        "outputId": "83f04866-d077-4434-f0ab-6d7e83ec09d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.543956\n",
              "1    0.456044\n",
              "Name: shipping, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train['shipping'].value_counts() / len(train)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gjn2r0dgk1OS"
      },
      "source": [
        "- Over 55% of items shipping fee were paid by the buyers. How shipping related to price?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "5FjuPjtrk1OT",
        "outputId": "52e40124-74a1-41a8-e0dd-f3679ef42d81"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "shipping_fee_by_buyer = train.loc[data['shipping'] == 0, 'price']\n",
        "shipping_fee_by_seller = train.loc[data['shipping'] == 1, 'price']\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(18,8))\n",
        "ax.hist(shipping_fee_by_seller, color='#8CB4E1', alpha=1.0, bins=50, range = [0, 100],\n",
        "       label='Price when Seller pays Shipping')\n",
        "ax.hist(shipping_fee_by_buyer, color='#007D00', alpha=0.7, bins=50, range = [0, 100],\n",
        "       label='Price when Buyer pays Shipping')\n",
        "plt.xlabel('price', fontsize=12)\n",
        "plt.ylabel('frequency', fontsize=12)\n",
        "plt.title('Price Distribution by Shipping Type', fontsize=15)\n",
        "plt.tick_params(labelsize=12)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMBqRllMk1OU",
        "outputId": "3dd2b783-6d31-4cf1-e536-effc51ac21d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average price is 22.03 if seller pays shipping\n",
            "The average price is 30.14 if buyer pays shipping\n"
          ]
        }
      ],
      "source": [
        "print('The average price is {}'.format(round(shipping_fee_by_seller.mean(), 2)), 'if seller pays shipping');\n",
        "print('The average price is {}'.format(round(shipping_fee_by_buyer.mean(), 2)), 'if buyer pays shipping')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JLyY8TRuk1OV"
      },
      "source": [
        "- We compare again after log-transformation on the price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "wh1YSgMOk1OW",
        "outputId": "7eb98721-9dcd-4709-dce4-824303783e21"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(18,8))\n",
        "ax.hist(np.log(shipping_fee_by_seller+1), color='#8CB4E1', alpha=1.0, bins=50,\n",
        "       label='Price when Seller pays Shipping')\n",
        "ax.hist(np.log(shipping_fee_by_buyer+1), color='#007D00', alpha=0.7, bins=50,\n",
        "       label='Price when Buyer pays Shipping')\n",
        "plt.xlabel('log(price+1)', fontsize=12)\n",
        "plt.ylabel('frequency', fontsize=12)\n",
        "plt.title('Price Distribution by Shipping Type', fontsize=15)\n",
        "plt.tick_params(labelsize=12)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUYHWN1wk1OX"
      },
      "source": [
        "- It is obvious that the average price is higher when buyer pays shipping."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz4PFBYGk1Oa"
      },
      "source": [
        "## Category names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqPHHdE9k1Ob",
        "outputId": "efd02299-90dd-40a3-bd79-ce2691c04e9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 591 unique values in category name column\n"
          ]
        }
      ],
      "source": [
        "print('There are', train['category_name'].nunique(), 'unique values in category name column')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnEZD65Mk1Od"
      },
      "source": [
        "- top 10 most common category names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZXZBR99k1Oe",
        "outputId": "8ce9a850-1568-4df5-9d43-df9a5e7b84e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Women/Athletic Apparel/Pants, Tights, Leggings                 309\n",
              "Women/Tops & Blouses/T-Shirts                                  245\n",
              "Beauty/Makeup/Face                                             200\n",
              "Beauty/Makeup/Lips                                             186\n",
              "Beauty/Makeup/Eyes                                             143\n",
              "Electronics/Cell Phones & Accessories/Cases, Covers & Skins    137\n",
              "Electronics/Video Games & Consoles/Games                       130\n",
              "Beauty/Fragrance/Women                                         117\n",
              "Women/Tops & Blouses/Tank, Cami                                114\n",
              "Women/Tops & Blouses/Blouse                                    113\n",
              "Name: category_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "train['category_name'].value_counts()[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVZxXWxZk1Of"
      },
      "source": [
        "## Item condition vs. Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "RKfN_qh8k1Og",
        "outputId": "d05eb83d-eb19-4ed4-9ae2-97d1822f08f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd54444efd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(x = 'item_condition_id', y = np.log(train['price']+1), data = train, palette = sns.color_palette('RdBu',5))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IggU67QDk1Og"
      },
      "source": [
        "# model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPZsfTjzk1Oh",
        "outputId": "413b4954-755c-4be1-8504-3763f7e1a219"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 41 items that do not have a category name.\n"
          ]
        }
      ],
      "source": [
        "print('There are %d items that do not have a category name.' %train['category_name'].isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTlyN2F9k1Oh",
        "outputId": "3f4ab6ce-dcd7-4693-9344-136abf8ecefb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 3430 items that do not have a brand name.\n"
          ]
        }
      ],
      "source": [
        "print('There are %d items that do not have a brand name.' %train['brand_name'].isnull().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tt_S0a9_k1Oi",
        "outputId": "d57d39e4-19c6-4f51-a4b2-b12074d3570e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 0 items that do not have a description.\n"
          ]
        }
      ],
      "source": [
        "print('There are %d items that do not have a description.' %train['item_description'].isnull().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "zFpjk-1Xk1Oj"
      },
      "outputs": [],
      "source": [
        "def handle_missing_inplace(dataset): \n",
        "    dataset['category_name'].fillna(value='missing', inplace=True) \n",
        "    dataset['brand_name'].fillna(value='missing', inplace=True) \n",
        "    dataset['item_description'].replace('No description yet,''missing', inplace=True) \n",
        "    dataset['item_description'].fillna(value='missing', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "blo4L-mSk1Ok"
      },
      "outputs": [],
      "source": [
        "def cutting(dataset):\n",
        "    pop_brand = dataset['brand_name'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_BRANDS]\n",
        "    dataset.loc[~dataset['brand_name'].isin(pop_brand), 'brand_name'] = 'missing'\n",
        "    pop_category = dataset['category_name'].value_counts().loc[lambda x: x.index != 'missing'].index[:NUM_CATEGORIES]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "VsbR18mWk1Ol"
      },
      "outputs": [],
      "source": [
        "def to_categorical(dataset):\n",
        "    dataset['category_name'] = dataset['category_name'].astype('category')\n",
        "    dataset['brand_name'] = dataset['brand_name'].astype('category')\n",
        "    dataset['item_condition_id'] = dataset['item_condition_id'].astype('category')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "GQoHSWaDk1Om"
      },
      "outputs": [],
      "source": [
        "train = train[train.price != 0].reset_index(drop=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "_UGExk_ok1On",
        "outputId": "56ca2728-8d3c-4fc4-df99-a745c2ad3b9c"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0  train_id                                 name  \\\n",
              "0              0         0  MLB Cincinnati Reds T Shirt Size XL   \n",
              "1              1         1     Razer BlackWidow Chroma Keyboard   \n",
              "2              2         2                       AVA-VIV Blouse   \n",
              "3              4         4                 24K GOLD plated rose   \n",
              "4              6         6   Acacia pacific tides santorini top   \n",
              "...          ...       ...                                  ...   \n",
              "7997        9995      9995              Halloween nail stickers   \n",
              "7998        9996      9996  NWT Wildfox Fringe Top XS Coachella   \n",
              "7999        9997      9997                Like New Baby Carrier   \n",
              "8000        9998      9998              Minecraft Creeper Scarf   \n",
              "8001       10000     10000            PINK Burgundy Windbreaker   \n",
              "\n",
              "      item_condition_id                                      category_name  \\\n",
              "0                     3                                  Men/Tops/T-shirts   \n",
              "1                     3  Electronics/Computers & Tablets/Components & P...   \n",
              "2                     1                        Women/Tops & Blouses/Blouse   \n",
              "3                     1                            Women/Jewelry/Necklaces   \n",
              "4                     3                           Women/Swimwear/Two-Piece   \n",
              "...                 ...                                                ...   \n",
              "7997                  1                                Beauty/Makeup/Nails   \n",
              "7998                  1                      Women/Tops & Blouses/T-Shirts   \n",
              "7999                  2                     Kids/Gear/Backpacks & Carriers   \n",
              "8000                  1                         Kids/Boys (4+)/Accessories   \n",
              "8001                  2                  Women/Coats & Jackets/Windbreaker   \n",
              "\n",
              "           brand_name  price  shipping  \\\n",
              "0                 NaN   10.0         1   \n",
              "1               Razer   52.0         0   \n",
              "2              Target   10.0         1   \n",
              "3                 NaN   44.0         0   \n",
              "4     Acacia Swimwear   64.0         0   \n",
              "...               ...    ...       ...   \n",
              "7997              NaN    3.0         1   \n",
              "7998  Wildfox Couture   17.0         0   \n",
              "7999           Chicco   15.0         1   \n",
              "8000              NaN   12.0         1   \n",
              "8001             PINK   79.0         0   \n",
              "\n",
              "                                       item_description  \n",
              "0                                    No description yet  \n",
              "1     This keyboard is in great condition and works ...  \n",
              "2     Adorable top with a hint of lace and a key hol...  \n",
              "3             Complete with certificate of authenticity  \n",
              "4     Size small but straps slightly shortened to fi...  \n",
              "...                                                 ...  \n",
              "7997  New. Both sets included *please only purchase ...  \n",
              "7998  NWT Wildfox Coachella Fringe Top size XS. Mint...  \n",
              "7999  Excellent condition. No tears no stains used m...  \n",
              "8000  A scarf is a trusty companion, covering your n...  \n",
              "8001  Size: Medium/ Large Worn twice Looks brand new...  \n",
              "\n",
              "[8002 rows x 9 columns]"
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              "      <td>Adorable top with a hint of lace and a key hol...</td>\n",
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              "      <td>Complete with certificate of authenticity</td>\n",
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              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>Acacia pacific tides santorini top</td>\n",
              "      <td>3</td>\n",
              "      <td>Women/Swimwear/Two-Piece</td>\n",
              "      <td>Acacia Swimwear</td>\n",
              "      <td>64.0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7997</th>\n",
              "      <td>9995</td>\n",
              "      <td>9995</td>\n",
              "      <td>Halloween nail stickers</td>\n",
              "      <td>1</td>\n",
              "      <td>Beauty/Makeup/Nails</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>New. Both sets included *please only purchase ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7998</th>\n",
              "      <td>9996</td>\n",
              "      <td>9996</td>\n",
              "      <td>NWT Wildfox Fringe Top XS Coachella</td>\n",
              "      <td>1</td>\n",
              "      <td>Women/Tops &amp; Blouses/T-Shirts</td>\n",
              "      <td>Wildfox Couture</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NWT Wildfox Coachella Fringe Top size XS. Mint...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7999</th>\n",
              "      <td>9997</td>\n",
              "      <td>9997</td>\n",
              "      <td>Like New Baby Carrier</td>\n",
              "      <td>2</td>\n",
              "      <td>Kids/Gear/Backpacks &amp; Carriers</td>\n",
              "      <td>Chicco</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Excellent condition. No tears no stains used m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8000</th>\n",
              "      <td>9998</td>\n",
              "      <td>9998</td>\n",
              "      <td>Minecraft Creeper Scarf</td>\n",
              "      <td>1</td>\n",
              "      <td>Kids/Boys (4+)/Accessories</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "train\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "XwqBVKGfk1Oo"
      },
      "outputs": [],
      "source": [
        "test_new = test.drop('price', axis=1)\n",
        "y_test = np.log1p(test[\"price\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "scrolled": true,
        "id": "qYL90Wl5k1Op"
      },
      "outputs": [],
      "source": [
        "nrow_train = train.shape[0]\n",
        "y = np.log1p(train[\"price\"])\n",
        "merge: pd.DataFrame = pd.concat([train, test_new])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "XTDjlL-Ik1Or"
      },
      "outputs": [],
      "source": [
        "NUM_BRANDS = 4000\n",
        "NUM_CATEGORIES = 1000\n",
        "NAME_MIN_DF = 10\n",
        "MAX_FEATURES_ITEM_DESCRIPTION = 50000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "scrolled": true,
        "id": "nl8P2bd_k1Os"
      },
      "outputs": [],
      "source": [
        "handle_missing_inplace(merge)\n",
        "cutting(merge)\n",
        "to_categorical(merge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "BBUpVWEBk1Ot"
      },
      "outputs": [],
      "source": [
        "cv = CountVectorizer(min_df=NAME_MIN_DF)\n",
        "X_name = cv.fit_transform(merge['name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "YwfXlhcXk1Ou"
      },
      "outputs": [],
      "source": [
        "cv = CountVectorizer()\n",
        "X_category = cv.fit_transform(merge['category_name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "3ZlzBGBak1Ou"
      },
      "outputs": [],
      "source": [
        "tv = TfidfVectorizer(max_features=MAX_FEATURES_ITEM_DESCRIPTION, ngram_range=(1, 3), stop_words='english')\n",
        "X_description = tv.fit_transform(merge['item_description'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "O4ze175Dk1Ov"
      },
      "outputs": [],
      "source": [
        "lb = LabelBinarizer(sparse_output=True)\n",
        "X_brand = lb.fit_transform(merge['brand_name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "41PjJrq9k1Ov"
      },
      "outputs": [],
      "source": [
        "X_dummies = pd.get_dummies(merge[['item_condition_id', 'shipping']], sparse=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M16WEkvbk1Ow",
        "outputId": "8f0a2d7b-5b03-4d2c-f709-6de317f98c70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shipping                          int64\n",
              "item_condition_id_1    Sparse[uint8, 0]\n",
              "item_condition_id_2    Sparse[uint8, 0]\n",
              "item_condition_id_3    Sparse[uint8, 0]\n",
              "item_condition_id_4    Sparse[uint8, 0]\n",
              "item_condition_id_5    Sparse[uint8, 0]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "X_dummies.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_dummies = csr_matrix(pd.get_dummies(merge[['item_condition_id', 'shipping']], sparse=True).values)\n"
      ],
      "metadata": {
        "id": "3ADjXmddqUma"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "3Ai_C5P8k1Ow"
      },
      "outputs": [],
      "source": [
        "sparse_merge = hstack(( X_dummies,X_description, X_brand, X_category, X_name)).tocsr()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1MQtggok1Ox",
        "outputId": "999df8ac-930b-43da-aefb-02191cdc98bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9995, 52192)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "sparse_merge.shape\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUNntT4Ok1Oy"
      },
      "source": [
        "Remove features with document frequency <=1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "Tklpytjik1Oy"
      },
      "outputs": [],
      "source": [
        "mask = np.array(np.clip(sparse_merge.getnnz(axis=0) - 1, 0, 1), dtype=bool)\n",
        "sparse_merge = sparse_merge[:, mask]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bg1d0ooCk1Oz",
        "outputId": "82d1e873-1c25-414a-a418-347bed2c1f31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9995, 31654)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "sparse_merge.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "KIdfGCBjk1Oz"
      },
      "outputs": [],
      "source": [
        "X = sparse_merge[:nrow_train]\n",
        "X_test = sparse_merge[nrow_train:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "JpblczFfk1O0"
      },
      "outputs": [],
      "source": [
        "train_X = lgb.Dataset(X, label=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "-mhLllQMk1O0"
      },
      "outputs": [],
      "source": [
        "params = {\n",
        "        'learning_rate': 0.75,\n",
        "        'application': 'regression',\n",
        "        'max_depth': 3,\n",
        "        'num_leaves': 100,\n",
        "        'verbosity': -1,\n",
        "        'metric': 'RMSE',\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbm = lgb.train(params, train_set=train_X, num_boost_round=3200, verbose_eval=100)"
      ],
      "metadata": {
        "id": "x53TUUMvpeDY"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)\n"
      ],
      "metadata": {
        "id": "bB7u9RtzpePr"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pW_tZRYOpeaN",
        "outputId": "b665aca9-d6e6-4bde-ff01-8505ad72253c"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.32790063, 3.25043029, 3.13475506, ..., 2.51126442, 3.83340136,\n",
              "       3.07458689])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3rGbxU5pek3",
        "outputId": "62114010-e468-4fa9-885a-ac78821b121c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 0.6783877979337455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jzJ7WKBepevX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yCjfZoZwpe6h"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "colab": {
      "name": "retail_price_optimization.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}