Product_Based_Browse_Node.ipynb

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  "metadata": {
    "colab": {
      "name": "engine.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPIz_ZOBSMU-",
        "outputId": "174f0363-3c16-47ad-8136-2a90c57b6202"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import csv\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import chi2\n",
        "import re\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "tTBsJUAVb6Xx",
        "outputId": "633d2041-56e9-4f5d-a133-6c7e7722168e"
      },
      "source": [
        "#test file\n",
        "path = \"/content/drive/MyDrive/Datasets/test.csv\"\n",
        "df_test = pd.read_csv(path, escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)\n",
        "df_test.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Command 3M Small Kitchen Hooks, White, Decorat...</td>\n",
              "      <td>Sale Unit: PACK</td>\n",
              "      <td>[INCLUDES - 9 hooks and 12 small indoor strips...</td>\n",
              "      <td>Command</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>O'Neal Jump Hardware JAG Unisex-Adult Glove (B...</td>\n",
              "      <td>Synthetic leather palm with double-layer thumb...</td>\n",
              "      <td>[Silicone printing for a better grip. Long las...</td>\n",
              "      <td>O'Neal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NFL Detroit Lions Portable Party Fridge, 15.8 ...</td>\n",
              "      <td>Boelter Brands lets you celebrate your favorit...</td>\n",
              "      <td>[Runs on 12 Volt DC Power or 110 Volt AC Power...</td>\n",
              "      <td>Boelter Brands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Panasonic Single Line KX-TS880MX Corded Phone ...</td>\n",
              "      <td>Features: 50 Station Phonebook Corded Phone Al...</td>\n",
              "      <td>Panasonic Landline Phones doesn't come with a ...</td>\n",
              "      <td>Panasonic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Zero Baby Girl's 100% Cotton Innerwear Bloomer...</td>\n",
              "      <td>Zero Baby Girl Panties Set. 100% Cotton, Breat...</td>\n",
              "      <td>[Zero Baby Girl Panties, Pack of 6, 100% Cotto...</td>\n",
              "      <td>Zero</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PRODUCT_ID  ...           BRAND\n",
              "0           1  ...         Command\n",
              "1           2  ...          O'Neal\n",
              "2           3  ...  Boelter Brands\n",
              "3           4  ...       Panasonic\n",
              "4           5  ...            Zero\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "q4uQ8Nfrgd9I",
        "outputId": "ff09accf-8e03-4a67-b203-9fdd9ff4f66a"
      },
      "source": [
        "#train file\n",
        "path = \"/content/drive/MyDrive/Datasets/train.csv\"\n",
        "df_train = pd.read_csv(path, escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)\n",
        "df_train = df_train.dropna()\n",
        "df_train.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "        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>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pete The Cat Bedtime Blues Doll, 14.5 Inch</td>\n",
              "      <td>Pete the Cat is the coolest, most popular cat ...</td>\n",
              "      <td>[Pete the Cat Bedtime Blues plush doll,Based o...</td>\n",
              "      <td>MerryMakers</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The New Yorker NYHM014 Refrigerator Magnet, 2 ...</td>\n",
              "      <td>The New Yorker Handsome Cello Wrapped Hard Mag...</td>\n",
              "      <td>[Cat In A Tea Cup by New Yorker cover artist G...</td>\n",
              "      <td>The New Yorker</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n",
              "      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n",
              "      <td>[Color: Blue,Sleeve: Full Sleeve,Material: Cot...</td>\n",
              "      <td>Bhavya Enterprise</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Glance Women's Wallet (Black) (LW-21)</td>\n",
              "      <td>This Black wallet by Glance will be a treasure...</td>\n",
              "      <td>[The Most Comfortable Women's Wallet That You ...</td>\n",
              "      <td>Glance</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Wild Animals Hungry Brain Educational Flash Ca...</td>\n",
              "      <td>Wild Animals are the animals that mostly stays...</td>\n",
              "      <td>[Playful learning: Flash cards develops the lo...</td>\n",
              "      <td>hungry brain</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               TITLE  ... BROWSE_NODE_ID\n",
              "0         Pete The Cat Bedtime Blues Doll, 14.5 Inch  ...              0\n",
              "1  The New Yorker NYHM014 Refrigerator Magnet, 2 ...  ...              1\n",
              "5  Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...  ...              5\n",
              "6              Glance Women's Wallet (Black) (LW-21)  ...              6\n",
              "7  Wild Animals Hungry Brain Educational Flash Ca...  ...              7\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfrjJPq8T8nZ",
        "outputId": "c6ae6051-73c4-4506-a93c-6dc5da96b52c"
      },
      "source": [
        "punctuation_signs = list(\"?:!.,;\")\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "nltk.download('stopwords')\n",
        "stop_words = list(stopwords.words('english'))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hw0_D27kT9AU"
      },
      "source": [
        "df_train['Title'] = df_train['TITLE'].str.replace(\"\\r\", \" \")\n",
        "df_train['Title'] = df_train['Title'].str.replace(\"\\n\", \" \")\n",
        "df_train['Title'] = df_train['Title'].str.replace(\"    \", \" \")\n",
        "df_train['Title'] = df_train['Title'].str.replace('\"', '')\n",
        "df_train['Title'] = df_train['Title'].str.lower()\n",
        "for punct_sign in punctuation_signs:\n",
        "  df_train['Title'] = df_train['Title'].str.replace(punct_sign, '')\n",
        "df_train['Title'] = df_train['Title'].str.replace(\"'s\", \"\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OU_-P-0UB_V"
      },
      "source": [
        "final_cols = [\"Title\", \"BROWSE_NODE_ID\"]\n",
        "df_train = df_train[final_cols]\n",
        "df_train = df_train.iloc[:35000, :]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EJDqp4ZULE_"
      },
      "source": [
        "df_test['Title'] = df_test['TITLE'].str.replace(\"\\r\", \" \")\n",
        "df_test['Title'] = df_test['Title'].str.replace(\"\\n\", \" \")\n",
        "df_test['Title'] = df_test['Title'].str.replace(\"    \", \" \")\n",
        "df_test['Title'] = df_test['Title'].str.replace('\"', '')\n",
        "df_test['Title'] = df_test['Title'].str.lower()\n",
        "for punct_sign in punctuation_signs:\n",
        "  df_test['Title'] = df_test['Title'].str.replace(punct_sign, '')\n",
        "df_test['Title'] = df_test['Title'].str.replace(\"'s\", \"\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iO2eSD_06gwR"
      },
      "source": [
        "final_cols = [\"Title\", \"PRODUCT_ID\"]\n",
        "df_test = df_test[final_cols]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "HcXWz9VgUgoR",
        "outputId": "fe862498-dfcb-4022-b59a-29f12238f83b"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 9,
      "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>Title</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pete the cat bedtime blues doll 145 inch</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the new yorker nyhm014 refrigerator magnet 2 x 35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>men full sleeve raglan t-shirts denim t-shirt ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>glance women wallet (black) (lw-21)</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>wild animals hungry brain educational flash ca...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Title  BROWSE_NODE_ID\n",
              "0           pete the cat bedtime blues doll 145 inch               0\n",
              "1  the new yorker nyhm014 refrigerator magnet 2 x 35               1\n",
              "5  men full sleeve raglan t-shirts denim t-shirt ...               5\n",
              "6                glance women wallet (black) (lw-21)               6\n",
              "7  wild animals hungry brain educational flash ca...               7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "0eAngSitUlP2",
        "outputId": "173baf1b-3f82-47db-de7f-5d76a6534015"
      },
      "source": [
        "df_test.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Title</th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>command 3m small kitchen hooks white decorate ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>o'neal jump hardware jag unisex-adult glove (b...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nfl detroit lions portable party fridge 158 quart</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>panasonic single line kx-ts880mx corded phone ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>zero baby girl 100% cotton innerwear bloomer d...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Title  PRODUCT_ID\n",
              "0  command 3m small kitchen hooks white decorate ...           1\n",
              "1  o'neal jump hardware jag unisex-adult glove (b...           2\n",
              "2  nfl detroit lions portable party fridge 158 quart           3\n",
              "3  panasonic single line kx-ts880mx corded phone ...           4\n",
              "4  zero baby girl 100% cotton innerwear bloomer d...           5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPq6ragPUmtD",
        "outputId": "d0da8e4d-dc01-43c7-824a-2f605dbf3697"
      },
      "source": [
        "df_train.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Title             0\n",
              "BROWSE_NODE_ID    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0y2bReP75ic"
      },
      "source": [
        "df_test[\"Title\"].fillna(\"No Data\", inplace = True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91d96DmBaKp6",
        "outputId": "8e61dd4e-8c44-4b1e-8b7b-e7205e223dd3"
      },
      "source": [
        "df_test.isna().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Title         0\n",
              "PRODUCT_ID    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4DVt4gsXQaX"
      },
      "source": [
        "X_train, X_test, y_train = df_train[\"Title\"], df_test[\"Title\"], df_train[\"BROWSE_NODE_ID\"]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5X_c72rUs1l",
        "outputId": "abd8aaef-f245-4e77-ff72-f9b2bb066067"
      },
      "source": [
        "tfidf = TfidfVectorizer(stop_words='english', ngram_range=(1,2), min_df=5)\n",
        "print('1')\n",
        "X_train_vectors_tfidf = tfidf.fit_transform(X_train)\n",
        "print(X_train_vectors_tfidf.shape)\n",
        "print('1')\n",
        "X_test_vectors_tfidf = tfidf.transform(X_test)\n",
        "print(X_test_vectors_tfidf.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "(35000, 16942)\n",
            "1\n",
            "(110775, 16942)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hO0q3lXFU2ZN",
        "outputId": "607a0b95-93aa-4076-b912-8dd7234b1b12"
      },
      "source": [
        "lr_tfidf=LogisticRegression(solver = 'liblinear', C=10, penalty = 'l2')\n",
        "lr_tfidf.fit(X_train_vectors_tfidf, y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpEPd5PxWude"
      },
      "source": [
        "y_predict = lr_tfidf.predict(X_test_vectors_tfidf)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSTJUhZtfLX4",
        "outputId": "69be8688-c9fc-4cfe-9f72-d5d137ee66fc"
      },
      "source": [
        "y_predict"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1140, 1045, 8269, ...,  800,  800,  800])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64QUo0xcfTJI",
        "outputId": "50e42da2-b50d-4f24-b23d-f1b19deb1f1b"
      },
      "source": [
        "len(y_predict)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110775"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cEEL7QfgBzn"
      },
      "source": [
        "d = {\"BROWSE_NODE_ID\" : y_predict}"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "8ymabN1ugEVa",
        "outputId": "58f65c77-f6d3-4d3c-cb60-eab928589a8f"
      },
      "source": [
        "df1 = pd.DataFrame(data=d)\n",
        "df1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1140</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110770</th>\n",
              "      <td>4368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110771</th>\n",
              "      <td>1551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110772</th>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110773</th>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110774</th>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110775 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        BROWSE_NODE_ID\n",
              "0                 1140\n",
              "1                 1045\n",
              "2                 8269\n",
              "3                  125\n",
              "4                 1922\n",
              "...                ...\n",
              "110770            4368\n",
              "110771            1551\n",
              "110772             800\n",
              "110773             800\n",
              "110774             800\n",
              "\n",
              "[110775 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "ZpXMka2cgqi9",
        "outputId": "585cdf6a-1719-477b-b764-1a9f57fbdc12"
      },
      "source": [
        "df_new = pd.concat([df_test, df1], axis = 1)\n",
        "df_new.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>command 3m small kitchen hooks white decorate ...</td>\n",
              "      <td>1</td>\n",
              "      <td>1140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>o'neal jump hardware jag unisex-adult glove (b...</td>\n",
              "      <td>2</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nfl detroit lions portable party fridge 158 quart</td>\n",
              "      <td>3</td>\n",
              "      <td>8269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>panasonic single line kx-ts880mx corded phone ...</td>\n",
              "      <td>4</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>zero baby girl 100% cotton innerwear bloomer d...</td>\n",
              "      <td>5</td>\n",
              "      <td>1922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Title  ...  BROWSE_NODE_ID\n",
              "0  command 3m small kitchen hooks white decorate ...  ...            1140\n",
              "1  o'neal jump hardware jag unisex-adult glove (b...  ...            1045\n",
              "2  nfl detroit lions portable party fridge 158 quart  ...            8269\n",
              "3  panasonic single line kx-ts880mx corded phone ...  ...             125\n",
              "4  zero baby girl 100% cotton innerwear bloomer d...  ...            1922\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "0UiIR1C3g0aL",
        "outputId": "a869900d-cd40-4364-a8b6-abd3ad6dd8af"
      },
      "source": [
        "l = [\"PRODUCT_ID\", \"BROWSE_NODE_ID\"]\n",
        "df_new = df_new[l]\n",
        "df_new"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>1045</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>8269</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110770</th>\n",
              "      <td>110771</td>\n",
              "      <td>4368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110771</th>\n",
              "      <td>110772</td>\n",
              "      <td>1551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110772</th>\n",
              "      <td>110773</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110773</th>\n",
              "      <td>110774</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110774</th>\n",
              "      <td>110775</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110775 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        PRODUCT_ID  BROWSE_NODE_ID\n",
              "0                1            1140\n",
              "1                2            1045\n",
              "2                3            8269\n",
              "3                4             125\n",
              "4                5            1922\n",
              "...            ...             ...\n",
              "110770      110771            4368\n",
              "110771      110772            1551\n",
              "110772      110773             800\n",
              "110773      110774             800\n",
              "110774      110775             800\n",
              "\n",
              "[110775 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTyuVvuPhgSE"
      },
      "source": [
        "df_new.to_csv(\"/content/drive/MyDrive/Datasets/submission.csv\", index = False, header = True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "u8MNaBoTiuYD",
        "outputId": "82ac3f23-96eb-4876-eda4-e51904beb539"
      },
      "source": [
        "path = \"/content/drive/MyDrive/Datasets/submission.csv\"\n",
        "df_sub = pd.read_csv(path)\n",
        "df_sub.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>8269</td>\n",
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              "      <th>3</th>\n",
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              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PRODUCT_ID  BROWSE_NODE_ID\n",
              "0           1            1140\n",
              "1           2            1045\n",
              "2           3            8269\n",
              "3           4             125\n",
              "4           5            1922"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALnvwky6rAA0",
        "outputId": "4d501fed-b5f8-44ab-9b52-c9d38c1a4911"
      },
      "source": [
        "df_sub.isna().sum()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PRODUCT_ID        0\n",
              "BROWSE_NODE_ID    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "m5FYWOJ3rD1x",
        "outputId": "84d5ac03-9cf2-41e7-a97b-cf1c550521c3"
      },
      "source": [
        "df_sub.describe()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>110775.000000</td>\n",
              "      <td>110775.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>55388.000000</td>\n",
              "      <td>1814.854317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31978.132372</td>\n",
              "      <td>2984.057629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27694.500000</td>\n",
              "      <td>507.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55388.000000</td>\n",
              "      <td>1045.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>83081.500000</td>\n",
              "      <td>1687.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>110775.000000</td>\n",
              "      <td>47970.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          PRODUCT_ID  BROWSE_NODE_ID\n",
              "count  110775.000000   110775.000000\n",
              "mean    55388.000000     1814.854317\n",
              "std     31978.132372     2984.057629\n",
              "min         1.000000        0.000000\n",
              "25%     27694.500000      507.000000\n",
              "50%     55388.000000     1045.000000\n",
              "75%     83081.500000     1687.000000\n",
              "max    110775.000000    47970.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tx07ygYxrGlu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}