packing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('/Users/namanmuktha/Desktop/cbithck/Amazon ML Challenge Dataset 2023/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1925202</td>\n",
       "      <td>ArtzFolio Tulip Flowers Blackout Curtain for D...</td>\n",
       "      <td>[LUXURIOUS &amp; APPEALING: Beautiful custom-made ...</td>\n",
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       "      <td>Marks &amp; Spencer Girls' Pyjama Sets T86_2561C_N...</td>\n",
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       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>PRIKNIK Horn Red Electric Air Horn Compressor ...</td>\n",
       "      <td>[Loud Dual Tone Trumpet Horn, Compatible With ...</td>\n",
       "      <td>Specifications: Color: Red, Material: Aluminiu...</td>\n",
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       "      <td>748.031495</td>\n",
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       "      <td>787.401574</td>\n",
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       "      <th>4</th>\n",
       "      <td>283658</td>\n",
       "      <td>The United Empire Loyalists: A Chronicle of th...</td>\n",
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      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
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       "1     2673191  Marks & Spencer Girls' Pyjama Sets T86_2561C_N...   \n",
       "2     2765088  PRIKNIK Horn Red Electric Air Horn Compressor ...   \n",
       "3     1594019  ALISHAH Women's Cotton Ankle Length Leggings C...   \n",
       "4      283658  The United Empire Loyalists: A Chronicle of th...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "0  [LUXURIOUS & APPEALING: Beautiful custom-made ...   \n",
       "1  [Harry Potter Hedwig Pyjamas (6-16 Yrs),100% c...   \n",
       "2  [Loud Dual Tone Trumpet Horn, Compatible With ...   \n",
       "3  [Made By 95%cotton and 5% Lycra which gives yo...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "0                                                NaN             1650   \n",
       "1                                                NaN             2755   \n",
       "2  Specifications: Color: Red, Material: Aluminiu...             7537   \n",
       "3  AISHAH Women's Lycra Cotton Ankel Leggings. Br...             2996   \n",
       "4                                                NaN             6112   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "0     2125.980000  \n",
       "1      393.700000  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "4      598.424000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2249698, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRODUCT_ID               0\n",
       "TITLE                   13\n",
       "BULLET_POINTS       837366\n",
       "DESCRIPTION        1157382\n",
       "PRODUCT_TYPE_ID          0\n",
       "PRODUCT_LENGTH           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1038458, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRODUCT_ID         0\n",
       "TITLE              0\n",
       "BULLET_POINTS      0\n",
       "DESCRIPTION        0\n",
       "PRODUCT_TYPE_ID    0\n",
       "PRODUCT_LENGTH     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRODUCT_ID           int64\n",
       "TITLE               object\n",
       "BULLET_POINTS       object\n",
       "DESCRIPTION         object\n",
       "PRODUCT_TYPE_ID      int64\n",
       "PRODUCT_LENGTH     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>PRIKNIK Horn Red Electric Air Horn Compressor ...</td>\n",
       "      <td>[Loud Dual Tone Trumpet Horn, Compatible With ...</td>\n",
       "      <td>Specifications: Color: Red, Material: Aluminiu...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>ALISHAH Women's Cotton Ankle Length Leggings C...</td>\n",
       "      <td>[Made By 95%cotton and 5% Lycra which gives yo...</td>\n",
       "      <td>AISHAH Women's Lycra Cotton Ankel Leggings. Br...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>HINS Metal Bucket Shape Plant Pot for Indoor &amp;...</td>\n",
       "      <td>[Simple and elegant, great for displaying indo...</td>\n",
       "      <td>HINS Brings you the most Elegant Looking Pot w...</td>\n",
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       "      <td>2026580</td>\n",
       "      <td>Delavala Self Adhesive Kitchen Backsplash Wall...</td>\n",
       "      <td>[HIGH QUALITY PVC MATERIAL: The kitchen alumin...</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;Aluminum Foil Stickers-good kitchen...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
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       "      <td>2998633</td>\n",
       "      <td>Hexwell Essential oil for Home Fragrance Oil A...</td>\n",
       "      <td>[100% Pure And Natural Essential Oil Or Fragra...</td>\n",
       "      <td>Transform your home, workplace or hotel room i...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
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      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  PRIKNIK Horn Red Electric Air Horn Compressor ...   \n",
       "3     1594019  ALISHAH Women's Cotton Ankle Length Leggings C...   \n",
       "5     2152929  HINS Metal Bucket Shape Plant Pot for Indoor &...   \n",
       "7     2026580  Delavala Self Adhesive Kitchen Backsplash Wall...   \n",
       "9     2998633  Hexwell Essential oil for Home Fragrance Oil A...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [Loud Dual Tone Trumpet Horn, Compatible With ...   \n",
       "3  [Made By 95%cotton and 5% Lycra which gives yo...   \n",
       "5  [Simple and elegant, great for displaying indo...   \n",
       "7  [HIGH QUALITY PVC MATERIAL: The kitchen alumin...   \n",
       "9  [100% Pure And Natural Essential Oil Or Fragra...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  Specifications: Color: Red, Material: Aluminiu...             7537   \n",
       "3  AISHAH Women's Lycra Cotton Ankel Leggings. Br...             2996   \n",
       "5  HINS Brings you the most Elegant Looking Pot w...             5725   \n",
       "7  <p><strong>Aluminum Foil Stickers-good kitchen...             6030   \n",
       "9  Transform your home, workplace or hotel room i...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.head(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TITLE']=df['TITLE'].str.lower()\n",
    "df['BULLET_POINTS']=df['BULLET_POINTS'].str.lower()\n",
    "df['DESCRIPTION']=df['DESCRIPTION'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2765088</td>\n",
       "      <td>priknik horn red electric air horn compressor ...</td>\n",
       "      <td>[loud dual tone trumpet horn, compatible with ...</td>\n",
       "      <td>specifications: color: red, material: aluminiu...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
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       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>alishah women's cotton ankle length leggings c...</td>\n",
       "      <td>[made by 95%cotton and 5% lycra which gives yo...</td>\n",
       "      <td>aishah women's lycra cotton ankel leggings. br...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>hins metal bucket shape plant pot for indoor &amp;...</td>\n",
       "      <td>[simple and elegant, great for displaying indo...</td>\n",
       "      <td>hins brings you the most elegant looking pot w...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026580</td>\n",
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       "      <td>[high quality pvc material: the kitchen alumin...</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;aluminum foil stickers-good kitchen...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>hexwell essential oil for home fragrance oil a...</td>\n",
       "      <td>[100% pure and natural essential oil or fragra...</td>\n",
       "      <td>transform your home, workplace or hotel room i...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
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      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  priknik horn red electric air horn compressor ...   \n",
       "3     1594019  alishah women's cotton ankle length leggings c...   \n",
       "5     2152929  hins metal bucket shape plant pot for indoor &...   \n",
       "7     2026580  delavala self adhesive kitchen backsplash wall...   \n",
       "9     2998633  hexwell essential oil for home fragrance oil a...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud dual tone trumpet horn, compatible with ...   \n",
       "3  [made by 95%cotton and 5% lycra which gives yo...   \n",
       "5  [simple and elegant, great for displaying indo...   \n",
       "7  [high quality pvc material: the kitchen alumin...   \n",
       "9  [100% pure and natural essential oil or fragra...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  specifications: color: red, material: aluminiu...             7537   \n",
       "3  aishah women's lycra cotton ankel leggings. br...             2996   \n",
       "5  hins brings you the most elegant looking pot w...             5725   \n",
       "7  <p><strong>aluminum foil stickers-good kitchen...             6030   \n",
       "9  transform your home, workplace or hotel room i...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TITLE']=df['TITLE'].apply(lambda x: ''.join([c for c in x if c not in string.punctuation]))\n",
    "df['BULLET_POINTS']=df['BULLET_POINTS'].apply(lambda x: ''.join([c for c in x if c not in string.punctuation]))\n",
    "df['DESCRIPTION']=df['DESCRIPTION'].apply(lambda x: ''.join([c for c in x if c not in string.punctuation]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2765088</td>\n",
       "      <td>priknik horn red electric air horn compressor ...</td>\n",
       "      <td>loud dual tone trumpet horn compatible with sx...</td>\n",
       "      <td>specifications color red material aluminium vo...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
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       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>alishah womens cotton ankle length leggings co...</td>\n",
       "      <td>made by 95cotton and 5 lycra which gives you 4...</td>\n",
       "      <td>aishah womens lycra cotton ankel leggings bran...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
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       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>hins metal bucket shape plant pot for indoor  ...</td>\n",
       "      <td>simple and elegant great for displaying indoor...</td>\n",
       "      <td>hins brings you the most elegant looking pot w...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
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       "      <td>high quality pvc material the kitchen aluminum...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>hexwell essential oil for home fragrance oil a...</td>\n",
       "      <td>100 pure and natural essential oil or fragranc...</td>\n",
       "      <td>transform your home workplace or hotel room in...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  priknik horn red electric air horn compressor ...   \n",
       "3     1594019  alishah womens cotton ankle length leggings co...   \n",
       "5     2152929  hins metal bucket shape plant pot for indoor  ...   \n",
       "7     2026580  delavala self adhesive kitchen backsplash wall...   \n",
       "9     2998633  hexwell essential oil for home fragrance oil a...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  loud dual tone trumpet horn compatible with sx...   \n",
       "3  made by 95cotton and 5 lycra which gives you 4...   \n",
       "5  simple and elegant great for displaying indoor...   \n",
       "7  high quality pvc material the kitchen aluminum...   \n",
       "9  100 pure and natural essential oil or fragranc...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  specifications color red material aluminium vo...             7537   \n",
       "3  aishah womens lycra cotton ankel leggings bran...             2996   \n",
       "5  hins brings you the most elegant looking pot w...             5725   \n",
       "7  pstrongaluminum foil stickersgood kitchen help...             6030   \n",
       "9  transform your home workplace or hotel room in...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['TITLE','BULLET_POINTS','DESCRIPTION']\n",
    "for x in cols:\n",
    "    df[x]=df[x].apply(lambda x: x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>[priknik, horn, red, electric, air, horn, comp...</td>\n",
       "      <td>[loud, dual, tone, trumpet, horn, compatible, ...</td>\n",
       "      <td>[specifications, color, red, material, alumini...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>[alishah, womens, cotton, ankle, length, leggi...</td>\n",
       "      <td>[made, by, 95cotton, and, 5, lycra, which, giv...</td>\n",
       "      <td>[aishah, womens, lycra, cotton, ankel, legging...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>[hins, metal, bucket, shape, plant, pot, for, ...</td>\n",
       "      <td>[simple, and, elegant, great, for, displaying,...</td>\n",
       "      <td>[hins, brings, you, the, most, elegant, lookin...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026580</td>\n",
       "      <td>[delavala, self, adhesive, kitchen, backsplash...</td>\n",
       "      <td>[high, quality, pvc, material, the, kitchen, a...</td>\n",
       "      <td>[pstrongaluminum, foil, stickersgood, kitchen,...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>[hexwell, essential, oil, for, home, fragrance...</td>\n",
       "      <td>[100, pure, and, natural, essential, oil, or, ...</td>\n",
       "      <td>[transform, your, home, workplace, or, hotel, ...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  [priknik, horn, red, electric, air, horn, comp...   \n",
       "3     1594019  [alishah, womens, cotton, ankle, length, leggi...   \n",
       "5     2152929  [hins, metal, bucket, shape, plant, pot, for, ...   \n",
       "7     2026580  [delavala, self, adhesive, kitchen, backsplash...   \n",
       "9     2998633  [hexwell, essential, oil, for, home, fragrance...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud, dual, tone, trumpet, horn, compatible, ...   \n",
       "3  [made, by, 95cotton, and, 5, lycra, which, giv...   \n",
       "5  [simple, and, elegant, great, for, displaying,...   \n",
       "7  [high, quality, pvc, material, the, kitchen, a...   \n",
       "9  [100, pure, and, natural, essential, oil, or, ...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  [specifications, color, red, material, alumini...             7537   \n",
       "3  [aishah, womens, lycra, cotton, ankel, legging...             2996   \n",
       "5  [hins, brings, you, the, most, elegant, lookin...             5725   \n",
       "7  [pstrongaluminum, foil, stickersgood, kitchen,...             6030   \n",
       "9  [transform, your, home, workplace, or, hotel, ...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/namanmuktha/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "import nltk \n",
    "nltk.download('stopwords')\n",
    "stop_words=set(stopwords.words('english'))\n",
    "for m in cols:\n",
    "    df[m] = df[m].apply(lambda x: [word for word in x if word not in stop_words])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>[priknik, horn, red, electric, air, horn, comp...</td>\n",
       "      <td>[loud, dual, tone, trumpet, horn, compatible, ...</td>\n",
       "      <td>[specifications, color, red, material, alumini...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>[alishah, womens, cotton, ankle, length, leggi...</td>\n",
       "      <td>[made, 95cotton, 5, lycra, gives, 4, ways, str...</td>\n",
       "      <td>[aishah, womens, lycra, cotton, ankel, legging...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>[hins, metal, bucket, shape, plant, pot, indoo...</td>\n",
       "      <td>[simple, elegant, great, displaying, indoor, p...</td>\n",
       "      <td>[hins, brings, elegant, looking, pot, stand, d...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026580</td>\n",
       "      <td>[delavala, self, adhesive, kitchen, backsplash...</td>\n",
       "      <td>[high, quality, pvc, material, kitchen, alumin...</td>\n",
       "      <td>[pstrongaluminum, foil, stickersgood, kitchen,...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>[hexwell, essential, oil, home, fragrance, oil...</td>\n",
       "      <td>[100, pure, natural, essential, oil, fragrance...</td>\n",
       "      <td>[transform, home, workplace, hotel, room, pers...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  [priknik, horn, red, electric, air, horn, comp...   \n",
       "3     1594019  [alishah, womens, cotton, ankle, length, leggi...   \n",
       "5     2152929  [hins, metal, bucket, shape, plant, pot, indoo...   \n",
       "7     2026580  [delavala, self, adhesive, kitchen, backsplash...   \n",
       "9     2998633  [hexwell, essential, oil, home, fragrance, oil...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud, dual, tone, trumpet, horn, compatible, ...   \n",
       "3  [made, 95cotton, 5, lycra, gives, 4, ways, str...   \n",
       "5  [simple, elegant, great, displaying, indoor, p...   \n",
       "7  [high, quality, pvc, material, kitchen, alumin...   \n",
       "9  [100, pure, natural, essential, oil, fragrance...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  [specifications, color, red, material, alumini...             7537   \n",
       "3  [aishah, womens, lycra, cotton, ankel, legging...             2996   \n",
       "5  [hins, brings, elegant, looking, pot, stand, d...             5725   \n",
       "7  [pstrongaluminum, foil, stickersgood, kitchen,...             6030   \n",
       "9  [transform, home, workplace, hotel, room, pers...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/namanmuktha/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer \n",
    "nltk.download('wordnet')\n",
    "lemmatizer=WordNetLemmatizer()\n",
    "for m in cols:\n",
    "    df[m] = df[m].apply(lambda x: [lemmatizer.lemmatize(word) for word in x]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>[priknik, horn, red, electric, air, horn, comp...</td>\n",
       "      <td>[loud, dual, tone, trumpet, horn, compatible, ...</td>\n",
       "      <td>[specification, color, red, material, aluminiu...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>[alishah, woman, cotton, ankle, length, leggin...</td>\n",
       "      <td>[made, 95cotton, 5, lycra, give, 4, way, stret...</td>\n",
       "      <td>[aishah, woman, lycra, cotton, ankel, legging,...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>[hin, metal, bucket, shape, plant, pot, indoor...</td>\n",
       "      <td>[simple, elegant, great, displaying, indoor, p...</td>\n",
       "      <td>[hin, brings, elegant, looking, pot, stand, du...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026580</td>\n",
       "      <td>[delavala, self, adhesive, kitchen, backsplash...</td>\n",
       "      <td>[high, quality, pvc, material, kitchen, alumin...</td>\n",
       "      <td>[pstrongaluminum, foil, stickersgood, kitchen,...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>[hexwell, essential, oil, home, fragrance, oil...</td>\n",
       "      <td>[100, pure, natural, essential, oil, fragrance...</td>\n",
       "      <td>[transform, home, workplace, hotel, room, pers...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  [priknik, horn, red, electric, air, horn, comp...   \n",
       "3     1594019  [alishah, woman, cotton, ankle, length, leggin...   \n",
       "5     2152929  [hin, metal, bucket, shape, plant, pot, indoor...   \n",
       "7     2026580  [delavala, self, adhesive, kitchen, backsplash...   \n",
       "9     2998633  [hexwell, essential, oil, home, fragrance, oil...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud, dual, tone, trumpet, horn, compatible, ...   \n",
       "3  [made, 95cotton, 5, lycra, give, 4, way, stret...   \n",
       "5  [simple, elegant, great, displaying, indoor, p...   \n",
       "7  [high, quality, pvc, material, kitchen, alumin...   \n",
       "9  [100, pure, natural, essential, oil, fragrance...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  [specification, color, red, material, aluminiu...             7537   \n",
       "3  [aishah, woman, lycra, cotton, ankel, legging,...             2996   \n",
       "5  [hin, brings, elegant, looking, pot, stand, du...             5725   \n",
       "7  [pstrongaluminum, foil, stickersgood, kitchen,...             6030   \n",
       "9  [transform, home, workplace, hotel, room, pers...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import SnowballStemmer\n",
    "lang=\"english\"\n",
    "steamer=SnowballStemmer(lang)\n",
    "def adding_stemming(tokens):\n",
    "    return [steamer.stem(token) for token in tokens]\n",
    "for m in  cols:\n",
    "    df[m]=df[m].apply(adding_stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\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",
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       "      <th></th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2765088</td>\n",
       "      <td>[priknik, horn, red, electr, air, horn, compre...</td>\n",
       "      <td>[loud, dual, tone, trumpet, horn, compat, sx4e...</td>\n",
       "      <td>[specif, color, red, materi, aluminium, voltag...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1594019</td>\n",
       "      <td>[alishah, woman, cotton, ankl, length, leg, co...</td>\n",
       "      <td>[made, 95cotton, 5, lycra, give, 4, way, stret...</td>\n",
       "      <td>[aishah, woman, lycra, cotton, ankel, leg, bra...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2152929</td>\n",
       "      <td>[hin, metal, bucket, shape, plant, pot, indoor...</td>\n",
       "      <td>[simpl, eleg, great, display, indoor, plant, l...</td>\n",
       "      <td>[hin, bring, eleg, look, pot, stand, durabl, l...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026580</td>\n",
       "      <td>[delavala, self, adhes, kitchen, backsplash, w...</td>\n",
       "      <td>[high, qualiti, pvc, materi, kitchen, aluminum...</td>\n",
       "      <td>[pstrongaluminum, foil, stickersgood, kitchen,...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2998633</td>\n",
       "      <td>[hexwel, essenti, oil, home, fragranc, oil, ar...</td>\n",
       "      <td>[100, pure, natur, essenti, oil, fragranc, oil...</td>\n",
       "      <td>[transform, home, workplac, hotel, room, perso...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  [priknik, horn, red, electr, air, horn, compre...   \n",
       "3     1594019  [alishah, woman, cotton, ankl, length, leg, co...   \n",
       "5     2152929  [hin, metal, bucket, shape, plant, pot, indoor...   \n",
       "7     2026580  [delavala, self, adhes, kitchen, backsplash, w...   \n",
       "9     2998633  [hexwel, essenti, oil, home, fragranc, oil, ar...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud, dual, tone, trumpet, horn, compat, sx4e...   \n",
       "3  [made, 95cotton, 5, lycra, give, 4, way, stret...   \n",
       "5  [simpl, eleg, great, display, indoor, plant, l...   \n",
       "7  [high, qualiti, pvc, materi, kitchen, aluminum...   \n",
       "9  [100, pure, natur, essenti, oil, fragranc, oil...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  [specif, color, red, materi, aluminium, voltag...             7537   \n",
       "3  [aishah, woman, lycra, cotton, ankel, leg, bra...             2996   \n",
       "5  [hin, bring, eleg, look, pot, stand, durabl, l...             5725   \n",
       "7  [pstrongaluminum, foil, stickersgood, kitchen,...             6030   \n",
       "9  [transform, home, workplac, hotel, room, perso...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "2      748.031495  \n",
       "3      787.401574  \n",
       "5      950.000000  \n",
       "7      984.251967  \n",
       "9      393.700787  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRODUCT_ID         0\n",
       "TITLE              0\n",
       "BULLET_POINTS      0\n",
       "DESCRIPTION        0\n",
       "PRODUCT_TYPE_ID    0\n",
       "PRODUCT_LENGTH     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_preprocessed=pd.read_csv('/Users/namanmuktha/Desktop/cbithck/Amazon ML Challenge Dataset 2023/preprocess.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "638185    1.000000e+00\n",
       "92636     1.000000e+00\n",
       "917532    1.000000e+00\n",
       "35474     1.000000e+00\n",
       "654092    1.000000e+00\n",
       "              ...     \n",
       "110054    1.774080e+08\n",
       "362224    2.249280e+08\n",
       "499842    3.937010e+08\n",
       "731493    4.803152e+08\n",
       "410191    1.885801e+09\n",
       "Name: PRODUCT_LENGTH, Length: 1038458, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['PRODUCT_LENGTH'].sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([' ', 'PRODUCT_ID', 'TITLE', 'BULLET_POINTS', 'DESCRIPTION',\n",
       "       'PRODUCT_TYPE_ID', 'PRODUCT_LENGTH'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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></th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2765088</td>\n",
       "      <td>['priknik', 'horn', 'red', 'electr', 'air', 'h...</td>\n",
       "      <td>['loud', 'dual', 'tone', 'trumpet', 'horn', 'c...</td>\n",
       "      <td>['specif', 'color', 'red', 'materi', 'aluminiu...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1594019</td>\n",
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       "      <td>['made', '95cotton', '5', 'lycra', 'give', '4'...</td>\n",
       "      <td>['aishah', 'woman', 'lycra', 'cotton', 'ankel'...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2152929</td>\n",
       "      <td>['hin', 'metal', 'bucket', 'shape', 'plant', '...</td>\n",
       "      <td>['simpl', 'eleg', 'great', 'display', 'indoor'...</td>\n",
       "      <td>['hin', 'bring', 'eleg', 'look', 'pot', 'stand...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>2026580</td>\n",
       "      <td>['delavala', 'self', 'adhes', 'kitchen', 'back...</td>\n",
       "      <td>['high', 'qualiti', 'pvc', 'materi', 'kitchen'...</td>\n",
       "      <td>['pstrongaluminum', 'foil', 'stickersgood', 'k...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>2998633</td>\n",
       "      <td>['hexwel', 'essenti', 'oil', 'home', 'fragranc...</td>\n",
       "      <td>['100', 'pure', 'natur', 'essenti', 'oil', 'fr...</td>\n",
       "      <td>['transform', 'home', 'workplac', 'hotel', 'ro...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PRODUCT_ID                                              TITLE  \\\n",
       "0  2     2765088  ['priknik', 'horn', 'red', 'electr', 'air', 'h...   \n",
       "1  3     1594019  ['alishah', 'woman', 'cotton', 'ankl', 'length...   \n",
       "2  5     2152929  ['hin', 'metal', 'bucket', 'shape', 'plant', '...   \n",
       "3  7     2026580  ['delavala', 'self', 'adhes', 'kitchen', 'back...   \n",
       "4  9     2998633  ['hexwel', 'essenti', 'oil', 'home', 'fragranc...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "0  ['loud', 'dual', 'tone', 'trumpet', 'horn', 'c...   \n",
       "1  ['made', '95cotton', '5', 'lycra', 'give', '4'...   \n",
       "2  ['simpl', 'eleg', 'great', 'display', 'indoor'...   \n",
       "3  ['high', 'qualiti', 'pvc', 'materi', 'kitchen'...   \n",
       "4  ['100', 'pure', 'natur', 'essenti', 'oil', 'fr...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "0  ['specif', 'color', 'red', 'materi', 'aluminiu...             7537   \n",
       "1  ['aishah', 'woman', 'lycra', 'cotton', 'ankel'...             2996   \n",
       "2  ['hin', 'bring', 'eleg', 'look', 'pot', 'stand...             5725   \n",
       "3  ['pstrongaluminum', 'foil', 'stickersgood', 'k...             6030   \n",
       "4  ['transform', 'home', 'workplac', 'hotel', 'ro...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
       "0      748.031495  \n",
       "1      787.401574  \n",
       "2      950.000000  \n",
       "3      984.251967  \n",
       "4      393.700787  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preprocessed=df_preprocessed.drop([' '],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=df_preprocessed[['PRODUCT_ID','PRODUCT_LENGTH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2765088</td>\n",
       "      <td>748.031495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1594019</td>\n",
       "      <td>787.401574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2152929</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026580</td>\n",
       "      <td>984.251967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2998633</td>\n",
       "      <td>393.700787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID  PRODUCT_LENGTH\n",
       "0     2765088      748.031495\n",
       "1     1594019      787.401574\n",
       "2     2152929      950.000000\n",
       "3     2026580      984.251967\n",
       "4     2998633      393.700787"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preprocessed['COMBINED']=df_preprocessed['TITLE']+\" \"+df_preprocessed['BULLET_POINTS']+\" \"+df_preprocessed[\"DESCRIPTION\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>PRODUCT_ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>BULLET_POINTS</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>PRODUCT_TYPE_ID</th>\n",
       "      <th>PRODUCT_LENGTH</th>\n",
       "      <th>COMBINED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2765088</td>\n",
       "      <td>['priknik', 'horn', 'red', 'electr', 'air', 'h...</td>\n",
       "      <td>['loud', 'dual', 'tone', 'trumpet', 'horn', 'c...</td>\n",
       "      <td>['specif', 'color', 'red', 'materi', 'aluminiu...</td>\n",
       "      <td>7537</td>\n",
       "      <td>748.031495</td>\n",
       "      <td>['priknik', 'horn', 'red', 'electr', 'air', 'h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1594019</td>\n",
       "      <td>['alishah', 'woman', 'cotton', 'ankl', 'length...</td>\n",
       "      <td>['made', '95cotton', '5', 'lycra', 'give', '4'...</td>\n",
       "      <td>['aishah', 'woman', 'lycra', 'cotton', 'ankel'...</td>\n",
       "      <td>2996</td>\n",
       "      <td>787.401574</td>\n",
       "      <td>['alishah', 'woman', 'cotton', 'ankl', 'length...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2152929</td>\n",
       "      <td>['hin', 'metal', 'bucket', 'shape', 'plant', '...</td>\n",
       "      <td>['simpl', 'eleg', 'great', 'display', 'indoor'...</td>\n",
       "      <td>['hin', 'bring', 'eleg', 'look', 'pot', 'stand...</td>\n",
       "      <td>5725</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>['hin', 'metal', 'bucket', 'shape', 'plant', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026580</td>\n",
       "      <td>['delavala', 'self', 'adhes', 'kitchen', 'back...</td>\n",
       "      <td>['high', 'qualiti', 'pvc', 'materi', 'kitchen'...</td>\n",
       "      <td>['pstrongaluminum', 'foil', 'stickersgood', 'k...</td>\n",
       "      <td>6030</td>\n",
       "      <td>984.251967</td>\n",
       "      <td>['delavala', 'self', 'adhes', 'kitchen', 'back...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2998633</td>\n",
       "      <td>['hexwel', 'essenti', 'oil', 'home', 'fragranc...</td>\n",
       "      <td>['100', 'pure', 'natur', 'essenti', 'oil', 'fr...</td>\n",
       "      <td>['transform', 'home', 'workplac', 'hotel', 'ro...</td>\n",
       "      <td>8201</td>\n",
       "      <td>393.700787</td>\n",
       "      <td>['hexwel', 'essenti', 'oil', 'home', 'fragranc...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1924576</td>\n",
       "      <td>['topow', 'farmhous', 'kitchen', 'stool', 'ind...</td>\n",
       "      <td>['ampl', 'height', 'abil', 'swivel', '360', 'd...</td>\n",
       "      <td>['bdescript', 'brbrthe', 'vintag', 'industri',...</td>\n",
       "      <td>8360</td>\n",
       "      <td>1120.000000</td>\n",
       "      <td>['topow', 'farmhous', 'kitchen', 'stool', 'ind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1025518</td>\n",
       "      <td>['peerless', '616wh160bg', 'univers', 'shower'...</td>\n",
       "      <td>['25', 'gallon', 'per', 'minuteful', 'bodi', '...</td>\n",
       "      <td>['product', 'descript', 'replac', 'shower', 'h...</td>\n",
       "      <td>10302</td>\n",
       "      <td>256.250000</td>\n",
       "      <td>['peerless', '616wh160bg', 'univers', 'shower'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2895912</td>\n",
       "      <td>['gadget', 'gear', 'vinyl', 'skin', 'back', 's...</td>\n",
       "      <td>['3d', 'textur', 'mobil', 'skin', 'gadgetgear'...</td>\n",
       "      <td>['gadgetgear', 'skin', 'come', 'truetextur', '...</td>\n",
       "      <td>2213</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>['gadget', 'gear', 'vinyl', 'skin', 'back', 's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2194128</td>\n",
       "      <td>['4', 'pack', 'boho', 'wide', 'headband', 'wom...</td>\n",
       "      <td>['great', 'materi', 'woman', 'headband', 'made...</td>\n",
       "      <td>['4', 'pack', 'boho', 'wide', 'headband', 'wom...</td>\n",
       "      <td>3359</td>\n",
       "      <td>866.141731</td>\n",
       "      <td>['4', 'pack', 'boho', 'wide', 'headband', 'wom...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2368376</td>\n",
       "      <td>['jehova', 'car', 'cover', 'compat', 'ford', '...</td>\n",
       "      <td>['vehicl', 'compat', 'exclus', 'car', 'cover',...</td>\n",
       "      <td>['dear', 'custom', 'pleas', 'read', 'complet',...</td>\n",
       "      <td>7332</td>\n",
       "      <td>1181.102361</td>\n",
       "      <td>['jehova', 'car', 'cover', 'compat', 'ford', '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PRODUCT_ID                                              TITLE  \\\n",
       "0       2765088  ['priknik', 'horn', 'red', 'electr', 'air', 'h...   \n",
       "1       1594019  ['alishah', 'woman', 'cotton', 'ankl', 'length...   \n",
       "2       2152929  ['hin', 'metal', 'bucket', 'shape', 'plant', '...   \n",
       "3       2026580  ['delavala', 'self', 'adhes', 'kitchen', 'back...   \n",
       "4       2998633  ['hexwel', 'essenti', 'oil', 'home', 'fragranc...   \n",
       "..          ...                                                ...   \n",
       "995     1924576  ['topow', 'farmhous', 'kitchen', 'stool', 'ind...   \n",
       "996     1025518  ['peerless', '616wh160bg', 'univers', 'shower'...   \n",
       "997     2895912  ['gadget', 'gear', 'vinyl', 'skin', 'back', 's...   \n",
       "998     2194128  ['4', 'pack', 'boho', 'wide', 'headband', 'wom...   \n",
       "999     2368376  ['jehova', 'car', 'cover', 'compat', 'ford', '...   \n",
       "\n",
       "                                         BULLET_POINTS  \\\n",
       "0    ['loud', 'dual', 'tone', 'trumpet', 'horn', 'c...   \n",
       "1    ['made', '95cotton', '5', 'lycra', 'give', '4'...   \n",
       "2    ['simpl', 'eleg', 'great', 'display', 'indoor'...   \n",
       "3    ['high', 'qualiti', 'pvc', 'materi', 'kitchen'...   \n",
       "4    ['100', 'pure', 'natur', 'essenti', 'oil', 'fr...   \n",
       "..                                                 ...   \n",
       "995  ['ampl', 'height', 'abil', 'swivel', '360', 'd...   \n",
       "996  ['25', 'gallon', 'per', 'minuteful', 'bodi', '...   \n",
       "997  ['3d', 'textur', 'mobil', 'skin', 'gadgetgear'...   \n",
       "998  ['great', 'materi', 'woman', 'headband', 'made...   \n",
       "999  ['vehicl', 'compat', 'exclus', 'car', 'cover',...   \n",
       "\n",
       "                                           DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "0    ['specif', 'color', 'red', 'materi', 'aluminiu...             7537   \n",
       "1    ['aishah', 'woman', 'lycra', 'cotton', 'ankel'...             2996   \n",
       "2    ['hin', 'bring', 'eleg', 'look', 'pot', 'stand...             5725   \n",
       "3    ['pstrongaluminum', 'foil', 'stickersgood', 'k...             6030   \n",
       "4    ['transform', 'home', 'workplac', 'hotel', 'ro...             8201   \n",
       "..                                                 ...              ...   \n",
       "995  ['bdescript', 'brbrthe', 'vintag', 'industri',...             8360   \n",
       "996  ['product', 'descript', 'replac', 'shower', 'h...            10302   \n",
       "997  ['gadgetgear', 'skin', 'come', 'truetextur', '...             2213   \n",
       "998  ['4', 'pack', 'boho', 'wide', 'headband', 'wom...             3359   \n",
       "999  ['dear', 'custom', 'pleas', 'read', 'complet',...             7332   \n",
       "\n",
       "     PRODUCT_LENGTH                                           COMBINED  \n",
       "0        748.031495  ['priknik', 'horn', 'red', 'electr', 'air', 'h...  \n",
       "1        787.401574  ['alishah', 'woman', 'cotton', 'ankl', 'length...  \n",
       "2        950.000000  ['hin', 'metal', 'bucket', 'shape', 'plant', '...  \n",
       "3        984.251967  ['delavala', 'self', 'adhes', 'kitchen', 'back...  \n",
       "4        393.700787  ['hexwel', 'essenti', 'oil', 'home', 'fragranc...  \n",
       "..              ...                                                ...  \n",
       "995     1120.000000  ['topow', 'farmhous', 'kitchen', 'stool', 'ind...  \n",
       "996      256.250000  ['peerless', '616wh160bg', 'univers', 'shower'...  \n",
       "997      700.000000  ['gadget', 'gear', 'vinyl', 'skin', 'back', 's...  \n",
       "998      866.141731  ['4', 'pack', 'boho', 'wide', 'headband', 'wom...  \n",
       "999     1181.102361  ['jehova', 'car', 'cover', 'compat', 'ford', '...  \n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      ['priknik', 'horn', 'red', 'electr', 'air', 'h...\n",
       "1      ['alishah', 'woman', 'cotton', 'ankl', 'length...\n",
       "2      ['hin', 'metal', 'bucket', 'shape', 'plant', '...\n",
       "3      ['delavala', 'self', 'adhes', 'kitchen', 'back...\n",
       "4      ['hexwel', 'essenti', 'oil', 'home', 'fragranc...\n",
       "                             ...                        \n",
       "995    ['topow', 'farmhous', 'kitchen', 'stool', 'ind...\n",
       "996    ['peerless', '616wh160bg', 'univers', 'shower'...\n",
       "997    ['gadget', 'gear', 'vinyl', 'skin', 'back', 's...\n",
       "998    ['4', 'pack', 'boho', 'wide', 'headband', 'wom...\n",
       "999    ['jehova', 'car', 'cover', 'compat', 'ford', '...\n",
       "Name: COMBINED, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['COMBINED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader as api\n",
    "\n",
    "# Load GloVe model (choose a model that fits your needs)\n",
    "glove_model = api.load(\"glove-wiki-gigaword-300\")  # 100-dimensional GloVe model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def document_vector(tokens):\n",
    "    # Filter tokens to ensure they are in the GloVe vocabulary\n",
    "    valid_tokens = [token for token in tokens if token in glove_model]\n",
    "    if not valid_tokens:\n",
    "        # Return a zero vector if no valid tokens are found\n",
    "        return np.zeros(glove_model.vector_size)\n",
    "    # Average the embeddings of valid tokens\n",
    "    return np.mean(glove_model[valid_tokens], axis=0)\n",
    "\n",
    "# Assuming 'COMBINED' is a list of tokens\n",
    "df_preprocessed['doc_vector'] = df_preprocessed['COMBINED'].apply(document_vector)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_preprocessed['doc_vector']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert list of vectors into a matrix\n",
    "feature_matrix = np.vstack(df_preprocessed['doc_vector'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 300)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(max_features=50000)  # Limit to top 100 features for simplicity\n",
    "tfidf_matrix = vectorizer.fit_transform(df_preprocessed['COMBINED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "product_id = df_preprocessed['PRODUCT_ID'].values.reshape(-1, 1)\n",
    "product_type_id = df_preprocessed['PRODUCT_TYPE_ID'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD(n_components=300)\n",
    "X_reduced = svd.fit_transform(feature_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qk/cbjnsy590pn24y_t34wv1t500000gn/T/ipykernel_50084/1993992006.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_preprocessed['gpt_embedding'] = df_preprocessed['COMBINED'].apply(lambda x: get_gpt_embeddings(x, model, tokenizer))\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import GPT2Tokenizer, GPT2Model\n",
    "# Load pre-trained GPT model and tokenizer\n",
    "model_name = 'gpt2'  # You can choose a different pre-trained model if needed\n",
    "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
    "model = GPT2Model.from_pretrained(model_name)\n",
    "\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "# Function to get GPT-2 embeddings for text data\n",
    "def get_gpt_embeddings(text, model, tokenizer):\n",
    "    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**inputs)\n",
    "    embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()\n",
    "    return embeddings\n",
    "\n",
    "# Apply the function to get embeddings for each text\n",
    "df_preprocessed['gpt_embedding'] = df_preprocessed['COMBINED'].apply(lambda x: get_gpt_embeddings(x, model, tokenizer))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    [-0.79854584, 0.5039604, 0.11545144, -0.176085...\n",
      "1    [-0.30609593, 0.45206687, 0.22416879, -0.06679...\n",
      "2    [-0.45811784, 0.4040954, 0.4536416, 0.00227931...\n",
      "3    [-0.2569862, 0.3355509, 0.33961976, -0.0529973...\n",
      "4    [-0.5701827, 0.4061478, 0.16793266, -0.1085318...\n",
      "Name: bert_embedding, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qk/cbjnsy590pn24y_t34wv1t500000gn/T/ipykernel_50084/1558847812.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_preprocessed['bert_embedding'] = df_preprocessed['COMBINED'].apply(lambda x: get_bert_embeddings(x, model, tokenizer))\n"
     ]
    }
   ],
   "source": [
    "from transformers import BertTokenizer, BertModel\n",
    "# Load pre-trained BERT model and tokenizer\n",
    "# Load pre-trained BERT model and tokenizer\n",
    "model_name = 'bert-base-uncased'\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "model = BertModel.from_pretrained(model_name)\n",
    "\n",
    "# Function to get BERT embeddings for text data\n",
    "def get_bert_embeddings(text, model, tokenizer):\n",
    "    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**inputs)\n",
    "    # Get the embeddings for the [CLS] token\n",
    "    embeddings = outputs.last_hidden_state[:, 0, :].squeeze().numpy()\n",
    "    return embeddings\n",
    "\n",
    "# Apply the function to get embeddings for each text\n",
    "df_preprocessed['bert_embedding'] = df_preprocessed['COMBINED'].apply(lambda x: get_bert_embeddings(x, model, tokenizer))\n",
    "\n",
    "# Verify if 'bert_embedding' column is correctly created\n",
    "print(df_preprocessed['bert_embedding'].head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "instructor-embedding is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py:286\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 286\u001b[0m     response\u001b[38;5;241m.\u001b[39mraise_for_status()\n\u001b[1;32m    287\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://huggingface.co/instructor-embedding/resolve/main/tokenizer_config.json",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/utils/hub.py:385\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    384\u001b[0m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 385\u001b[0m     resolved_file \u001b[38;5;241m=\u001b[39m hf_hub_download(\n\u001b[1;32m    386\u001b[0m         path_or_repo_id,\n\u001b[1;32m    387\u001b[0m         filename,\n\u001b[1;32m    388\u001b[0m         subfolder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(subfolder) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m subfolder,\n\u001b[1;32m    389\u001b[0m         repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[1;32m    390\u001b[0m         revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m    391\u001b[0m         cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m    392\u001b[0m         user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[1;32m    393\u001b[0m         force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m    394\u001b[0m         proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m    395\u001b[0m         resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[1;32m    396\u001b[0m         token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m    397\u001b[0m         local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m    398\u001b[0m     )\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m GatedRepoError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:1368\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(head_call_error, RepositoryNotFoundError) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(head_call_error, GatedRepoError):\n\u001b[1;32m   1367\u001b[0m     \u001b[38;5;66;03m# Repo not found => let's raise the actual error\u001b[39;00m\n\u001b[0;32m-> 1368\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m head_call_error\n\u001b[1;32m   1369\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1370\u001b[0m     \u001b[38;5;66;03m# Otherwise: most likely a connection issue or Hub downtime => let's warn the user\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:1238\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m   1237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1238\u001b[0m     metadata \u001b[38;5;241m=\u001b[39m get_hf_file_metadata(\n\u001b[1;32m   1239\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m   1240\u001b[0m         token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m   1241\u001b[0m         proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m   1242\u001b[0m         timeout\u001b[38;5;241m=\u001b[39metag_timeout,\n\u001b[1;32m   1243\u001b[0m         library_name\u001b[38;5;241m=\u001b[39mlibrary_name,\n\u001b[1;32m   1244\u001b[0m         library_version\u001b[38;5;241m=\u001b[39mlibrary_version,\n\u001b[1;32m   1245\u001b[0m         user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[1;32m   1246\u001b[0m     )\n\u001b[1;32m   1247\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m http_error:\n\u001b[1;32m   1248\u001b[0m     \u001b[38;5;66;03m# Cache the non-existence of the file and raise\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:1631\u001b[0m, in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;66;03m# Retrieve metadata\u001b[39;00m\n\u001b[0;32m-> 1631\u001b[0m r \u001b[38;5;241m=\u001b[39m _request_wrapper(\n\u001b[1;32m   1632\u001b[0m     method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHEAD\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1633\u001b[0m     url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m   1634\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m   1635\u001b[0m     allow_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1636\u001b[0m     follow_relative_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m   1637\u001b[0m     proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m   1638\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[1;32m   1639\u001b[0m )\n\u001b[1;32m   1640\u001b[0m hf_raise_for_status(r)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:385\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m follow_relative_redirects:\n\u001b[0;32m--> 385\u001b[0m     response \u001b[38;5;241m=\u001b[39m _request_wrapper(\n\u001b[1;32m    386\u001b[0m         method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m    387\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m    388\u001b[0m         follow_relative_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams,\n\u001b[1;32m    390\u001b[0m     )\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;66;03m# If redirection, we redirect only relative paths.\u001b[39;00m\n\u001b[1;32m    393\u001b[0m     \u001b[38;5;66;03m# This is useful in case of a renamed repository.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/file_download.py:409\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    408\u001b[0m response \u001b[38;5;241m=\u001b[39m get_session()\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m--> 409\u001b[0m hf_raise_for_status(response)\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py:323\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    315\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    316\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    317\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m make sure you are authenticated.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    322\u001b[0m     )\n\u001b[0;32m--> 323\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RepositoryNotFoundError(message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m400\u001b[39m:\n",
      "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 401 Client Error. (Request ID: Root=1-664db9ed-606776f265ab8349295ddcdb;918a2732-e52a-40d6-b28f-e613be58bc78)\n\nRepository Not Found for url: https://huggingface.co/instructor-embedding/resolve/main/tokenizer_config.json.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nInvalid username or password.",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Load pre-trained Instructor model and tokenizer\u001b[39;00m\n\u001b[1;32m      7\u001b[0m model_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minstructor-embedding\u001b[39m\u001b[38;5;124m'\u001b[39m  \u001b[38;5;66;03m# Replace with the actual model name\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_name)\n\u001b[1;32m      9\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModel\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_name)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Function to get Instructor embeddings for text data\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py:758\u001b[0m, in \u001b[0;36mAutoTokenizer.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    755\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tokenizer_class\u001b[38;5;241m.\u001b[39mfrom_pretrained(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39minputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    757\u001b[0m \u001b[38;5;66;03m# Next, let's try to use the tokenizer_config file to get the tokenizer class.\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m tokenizer_config \u001b[38;5;241m=\u001b[39m get_tokenizer_config(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m tokenizer_config:\n\u001b[1;32m    760\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m tokenizer_config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py:590\u001b[0m, in \u001b[0;36mget_tokenizer_config\u001b[0;34m(pretrained_model_name_or_path, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    587\u001b[0m     token \u001b[38;5;241m=\u001b[39m use_auth_token\n\u001b[1;32m    589\u001b[0m commit_hash \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 590\u001b[0m resolved_config_file \u001b[38;5;241m=\u001b[39m cached_file(\n\u001b[1;32m    591\u001b[0m     pretrained_model_name_or_path,\n\u001b[1;32m    592\u001b[0m     TOKENIZER_CONFIG_FILE,\n\u001b[1;32m    593\u001b[0m     cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m    594\u001b[0m     force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m    595\u001b[0m     resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[1;32m    596\u001b[0m     proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m    597\u001b[0m     token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m    598\u001b[0m     revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m    599\u001b[0m     local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m    600\u001b[0m     subfolder\u001b[38;5;241m=\u001b[39msubfolder,\n\u001b[1;32m    601\u001b[0m     _raise_exceptions_for_missing_entries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    602\u001b[0m     _raise_exceptions_for_connection_errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    603\u001b[0m     _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[1;32m    604\u001b[0m )\n\u001b[1;32m    605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resolved_config_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    606\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not locate the tokenizer configuration file, will try to use the model config instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/utils/hub.py:406\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    401\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are trying to access a gated repo.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMake sure to request access at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    402\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and pass a token having permission to this repo either \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    403\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby logging in with `huggingface-cli login` or by passing `token=<your_token>`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    404\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 406\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    407\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a local folder and is not a valid model identifier \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    408\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlisted on \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/models\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mIf this is a private repository, make sure to pass a token \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    409\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhaving permission to this repo either by logging in with `huggingface-cli login` or by passing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    410\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`token=<your_token>`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    411\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RevisionNotFoundError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    413\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrevision\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a valid git identifier (branch name, tag name or commit id) that exists \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor this model name. Check the model page at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://huggingface.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for available revisions.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    417\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mOSError\u001b[0m: instructor-embedding is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>`"
     ]
    }
   ],
   "source": [
    "# Example installation, if necessary\n",
    "# !pip install instructor-embedding\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "\n",
    "# Load pre-trained Instructor model and tokenizer\n",
    "model_name = 'instructor-embedding'  # Replace with the actual model name\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModel.from_pretrained(model_name)\n",
    "\n",
    "# Function to get Instructor embeddings for text data\n",
    "def get_instructor_embeddings(text, model, tokenizer):\n",
    "    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**inputs)\n",
    "    # Get the embeddings for the [CLS] token (or appropriate token for Instructor embeddings)\n",
    "        embeddings = outputs.last_hidden_state[:, 0, :].squeeze().numpy()\n",
    "    return embeddings\n",
    "\n",
    "# Apply the function to get embeddings for each text\n",
    "df_preprocessed['instructor_embedding'] = df_preprocessed['COMBINED'].apply(lambda x: get_instructor_embeddings(x, model, tokenizer))\n",
    "\n",
    "# Verify if 'instructor_embedding' column is correctly created\n",
    "print(df_preprocessed['instructor_embedding'].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [-0.79854584, 0.5039604, 0.11545144, -0.176085...\n",
       "1      [-0.30609593, 0.45206687, 0.22416879, -0.06679...\n",
       "2      [-0.45811784, 0.4040954, 0.4536416, 0.00227931...\n",
       "3      [-0.2569862, 0.3355509, 0.33961976, -0.0529973...\n",
       "4      [-0.5701827, 0.4061478, 0.16793266, -0.1085318...\n",
       "                             ...                        \n",
       "995    [-0.4235385, 0.36414298, 0.2643394, -0.1386073...\n",
       "996    [-0.30066976, 0.64854145, 0.39408708, -0.00065...\n",
       "997    [-0.33978215, 0.48002696, 0.3680922, 0.2128662...\n",
       "998    [-0.87551904, 0.14078686, -0.02929226, -0.3257...\n",
       "999    [-0.6438004, 0.48500547, 0.18298662, -0.196143...\n",
       "Name: bert_embedding, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['bert_embedding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert embeddings to a numpy array\n",
    "embeddings = np.vstack(df_preprocessed['gpt_embedding'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_features=np.hstack((embeddings,np.vstack(df_preprocessed['bert_embedding'].values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02320801, -0.14110456, -0.35508487, ..., -0.3462479 ,\n",
       "         0.36017126,  0.5288499 ],\n",
       "       [-0.12109176,  0.17225403, -0.72666144, ..., -0.32833835,\n",
       "         0.2038836 ,  0.16749488],\n",
       "       [ 0.01393954, -0.12326314, -0.52447367, ..., -0.16595463,\n",
       "        -0.00079611,  0.67962134],\n",
       "       ...,\n",
       "       [ 0.00372659, -0.03251521, -0.3652771 , ...,  0.07635952,\n",
       "        -0.13696037,  0.64622134],\n",
       "       [ 0.07358525, -0.1186664 , -0.6600033 , ..., -0.62903696,\n",
       "         0.13857451,  0.3226976 ],\n",
       "       [ 0.09508742, -0.05480634, -0.423302  , ..., -0.13424127,\n",
       "        -0.0448215 ,  0.6511184 ]], dtype=float32)"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduced=df_preprocessed[['gpt_embedding']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       748.031495\n",
       "1       787.401574\n",
       "2       950.000000\n",
       "3       984.251967\n",
       "4       393.700787\n",
       "          ...     \n",
       "995    1120.000000\n",
       "996     256.250000\n",
       "997     700.000000\n",
       "998     866.141731\n",
       "999    1181.102361\n",
       "Name: PRODUCT_LENGTH, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed['PRODUCT_LENGTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(combined_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1536)"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/namanmuktha/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=0)\n",
    "clusters = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# Visualizing clusters can be done using PCA to reduce dimensions to 2D for plotting\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=500)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=clusters, cmap='viridis', marker='o', edgecolor='black', s=50, alpha=0.6)\n",
    "plt.title('Visualization of Clustered Data')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.589601  ,  12.515272  ,  11.847268  , ...,  -0.0564213 ,\n",
       "          0.691849  ,   0.36828157],\n",
       "       [ -7.309293  ,  -0.5705514 , -13.550643  , ...,   0.10954434,\n",
       "          0.49541575,  -0.25546032],\n",
       "       [ -8.671579  ,  -1.6777622 ,  -1.1155766 , ...,   0.04960076,\n",
       "         -0.08770578,  -0.02972106],\n",
       "       ...,\n",
       "       [-17.760874  ,  -7.2864704 ,   4.484987  , ...,   0.162794  ,\n",
       "          0.73923326,   0.32527658],\n",
       "       [  1.3996328 ,  16.653782  , -14.497782  , ...,   0.45777062,\n",
       "          0.42398092,   0.10535198],\n",
       "       [  4.239429  ,  -2.4113708 ,   1.8614022 , ...,   0.6718418 ,\n",
       "         -0.3114977 ,  -0.10323139]], dtype=float32)"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD(n_components=600)\n",
    "X_reduced = svd.fit_transform(embeddings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout,BatchNormalization\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(512, activation='relu', input_dim=X_pca.shape[1]),  # Adjusted input dimension\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.2),\n",
    "    Dense(256, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.2),\n",
    "    Dense(128, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.2),\n",
    "    Dense(64, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.2),\n",
    "    Dense(32, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.1),\n",
    "    Dense(1, activation='linear')  # Output layer for regression\n",
    "])\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='mean_absolute_error',  # Common loss function for regression\n",
    "              metrics=['mean_absolute_error'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=target['PRODUCT_LENGTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.4002792e+02,  1.3848491e+00,  9.5007133e-01, ...,\n",
       "         4.4779107e-03,  4.9288571e-03, -1.1844322e-02],\n",
       "       [ 1.5610638e+02, -1.0467092e+00,  2.9242964e+00, ...,\n",
       "        -3.5010837e-03, -7.8267641e-03, -1.5172442e-02],\n",
       "       [ 1.4678346e+02,  2.8367794e-01, -2.0366874e+00, ...,\n",
       "        -1.2650654e-02, -4.0117502e-03, -1.2389593e-02],\n",
       "       ...,\n",
       "       [ 1.6583034e+02,  1.0508527e+01, -2.5168705e-01, ...,\n",
       "         4.9542091e-03, -3.1275274e-03, -2.0386279e-02],\n",
       "       [ 1.5049501e+02,  5.9903536e+00, -9.8642349e-01, ...,\n",
       "         8.1935525e-04, -7.1444795e-03,  2.0059973e-02],\n",
       "       [ 1.4366240e+02,  2.8376715e+00, -1.5417700e+00, ...,\n",
       "         1.0051428e-02,  3.5641422e-03,  6.4171068e-03]], dtype=float32)"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 500)\n",
      "(200, 500)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)  # Should show (number_of_samples, 302)\n",
    "print(X_test.shape)   # Should show (number_of_samples, 302)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1536)"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "25/25 [==============================] - 3s 18ms/step - loss: 1330.1455 - mean_absolute_error: 1330.1455 - val_loss: 1209.1870 - val_mean_absolute_error: 1209.1870\n",
      "Epoch 2/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1329.9700 - mean_absolute_error: 1329.9700 - val_loss: 1208.9404 - val_mean_absolute_error: 1208.9404\n",
      "Epoch 3/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1329.7019 - mean_absolute_error: 1329.7019 - val_loss: 1208.7230 - val_mean_absolute_error: 1208.7230\n",
      "Epoch 4/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1329.4374 - mean_absolute_error: 1329.4374 - val_loss: 1208.4270 - val_mean_absolute_error: 1208.4270\n",
      "Epoch 5/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1329.0476 - mean_absolute_error: 1329.0476 - val_loss: 1208.0685 - val_mean_absolute_error: 1208.0685\n",
      "Epoch 6/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1328.6544 - mean_absolute_error: 1328.6544 - val_loss: 1207.3195 - val_mean_absolute_error: 1207.3195\n",
      "Epoch 7/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1328.0685 - mean_absolute_error: 1328.0685 - val_loss: 1206.4325 - val_mean_absolute_error: 1206.4325\n",
      "Epoch 8/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1327.4235 - mean_absolute_error: 1327.4235 - val_loss: 1205.9114 - val_mean_absolute_error: 1205.9114\n",
      "Epoch 9/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1326.7091 - mean_absolute_error: 1326.7091 - val_loss: 1205.1257 - val_mean_absolute_error: 1205.1257\n",
      "Epoch 10/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1325.8068 - mean_absolute_error: 1325.8068 - val_loss: 1204.1652 - val_mean_absolute_error: 1204.1652\n",
      "Epoch 11/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1324.8422 - mean_absolute_error: 1324.8422 - val_loss: 1203.2827 - val_mean_absolute_error: 1203.2827\n",
      "Epoch 12/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1323.7711 - mean_absolute_error: 1323.7711 - val_loss: 1202.3430 - val_mean_absolute_error: 1202.3430\n",
      "Epoch 13/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1322.5842 - mean_absolute_error: 1322.5842 - val_loss: 1201.0472 - val_mean_absolute_error: 1201.0472\n",
      "Epoch 14/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1321.4353 - mean_absolute_error: 1321.4353 - val_loss: 1199.6709 - val_mean_absolute_error: 1199.6709\n",
      "Epoch 15/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1320.0138 - mean_absolute_error: 1320.0138 - val_loss: 1198.2571 - val_mean_absolute_error: 1198.2571\n",
      "Epoch 16/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1318.4860 - mean_absolute_error: 1318.4860 - val_loss: 1196.7959 - val_mean_absolute_error: 1196.7959\n",
      "Epoch 17/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1316.9183 - mean_absolute_error: 1316.9183 - val_loss: 1194.5925 - val_mean_absolute_error: 1194.5925\n",
      "Epoch 18/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1315.1462 - mean_absolute_error: 1315.1462 - val_loss: 1192.1489 - val_mean_absolute_error: 1192.1489\n",
      "Epoch 19/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1313.5490 - mean_absolute_error: 1313.5490 - val_loss: 1190.0647 - val_mean_absolute_error: 1190.0647\n",
      "Epoch 20/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1311.5142 - mean_absolute_error: 1311.5142 - val_loss: 1188.5175 - val_mean_absolute_error: 1188.5175\n",
      "Epoch 21/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1309.3610 - mean_absolute_error: 1309.3610 - val_loss: 1186.9487 - val_mean_absolute_error: 1186.9487\n",
      "Epoch 22/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1307.2761 - mean_absolute_error: 1307.2761 - val_loss: 1184.6936 - val_mean_absolute_error: 1184.6936\n",
      "Epoch 23/200\n",
      "25/25 [==============================] - 0s 6ms/step - loss: 1305.0787 - mean_absolute_error: 1305.0787 - val_loss: 1182.2327 - val_mean_absolute_error: 1182.2327\n",
      "Epoch 24/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1302.6465 - mean_absolute_error: 1302.6465 - val_loss: 1179.8992 - val_mean_absolute_error: 1179.8992\n",
      "Epoch 25/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1300.2914 - mean_absolute_error: 1300.2914 - val_loss: 1177.7811 - val_mean_absolute_error: 1177.7811\n",
      "Epoch 26/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1297.8417 - mean_absolute_error: 1297.8417 - val_loss: 1173.9955 - val_mean_absolute_error: 1173.9955\n",
      "Epoch 27/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1295.1960 - mean_absolute_error: 1295.1960 - val_loss: 1171.6140 - val_mean_absolute_error: 1171.6140\n",
      "Epoch 28/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1292.6755 - mean_absolute_error: 1292.6755 - val_loss: 1169.0283 - val_mean_absolute_error: 1169.0283\n",
      "Epoch 29/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1290.0126 - mean_absolute_error: 1290.0126 - val_loss: 1165.9126 - val_mean_absolute_error: 1165.9126\n",
      "Epoch 30/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1287.0571 - mean_absolute_error: 1287.0571 - val_loss: 1162.5431 - val_mean_absolute_error: 1162.5431\n",
      "Epoch 31/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1283.7616 - mean_absolute_error: 1283.7616 - val_loss: 1160.0737 - val_mean_absolute_error: 1160.0737\n",
      "Epoch 32/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1280.7985 - mean_absolute_error: 1280.7985 - val_loss: 1156.5264 - val_mean_absolute_error: 1156.5264\n",
      "Epoch 33/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1277.6727 - mean_absolute_error: 1277.6727 - val_loss: 1154.0323 - val_mean_absolute_error: 1154.0323\n",
      "Epoch 34/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1274.5369 - mean_absolute_error: 1274.5369 - val_loss: 1151.8778 - val_mean_absolute_error: 1151.8778\n",
      "Epoch 35/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1271.0590 - mean_absolute_error: 1271.0590 - val_loss: 1148.0953 - val_mean_absolute_error: 1148.0953\n",
      "Epoch 36/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1267.5452 - mean_absolute_error: 1267.5452 - val_loss: 1144.3628 - val_mean_absolute_error: 1144.3628\n",
      "Epoch 37/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1264.1826 - mean_absolute_error: 1264.1826 - val_loss: 1140.7740 - val_mean_absolute_error: 1140.7740\n",
      "Epoch 38/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1260.5381 - mean_absolute_error: 1260.5381 - val_loss: 1140.1930 - val_mean_absolute_error: 1140.1930\n",
      "Epoch 39/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1256.9893 - mean_absolute_error: 1256.9893 - val_loss: 1139.2566 - val_mean_absolute_error: 1139.2566\n",
      "Epoch 40/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1253.4431 - mean_absolute_error: 1253.4431 - val_loss: 1134.8185 - val_mean_absolute_error: 1134.8185\n",
      "Epoch 41/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1249.2080 - mean_absolute_error: 1249.2080 - val_loss: 1128.5118 - val_mean_absolute_error: 1128.5118\n",
      "Epoch 42/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1245.6843 - mean_absolute_error: 1245.6843 - val_loss: 1123.2596 - val_mean_absolute_error: 1123.2596\n",
      "Epoch 43/200\n",
      "25/25 [==============================] - 0s 6ms/step - loss: 1241.3282 - mean_absolute_error: 1241.3282 - val_loss: 1120.9897 - val_mean_absolute_error: 1120.9897\n",
      "Epoch 44/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1237.3451 - mean_absolute_error: 1237.3451 - val_loss: 1119.5946 - val_mean_absolute_error: 1119.5946\n",
      "Epoch 45/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1233.1548 - mean_absolute_error: 1233.1548 - val_loss: 1114.3203 - val_mean_absolute_error: 1114.3203\n",
      "Epoch 46/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1229.4194 - mean_absolute_error: 1229.4194 - val_loss: 1109.3447 - val_mean_absolute_error: 1109.3447\n",
      "Epoch 47/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1224.8059 - mean_absolute_error: 1224.8059 - val_loss: 1104.7877 - val_mean_absolute_error: 1104.7877\n",
      "Epoch 48/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1219.8521 - mean_absolute_error: 1219.8521 - val_loss: 1103.1333 - val_mean_absolute_error: 1103.1333\n",
      "Epoch 49/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1215.8005 - mean_absolute_error: 1215.8005 - val_loss: 1100.8009 - val_mean_absolute_error: 1100.8009\n",
      "Epoch 50/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1211.2433 - mean_absolute_error: 1211.2433 - val_loss: 1096.1979 - val_mean_absolute_error: 1096.1979\n",
      "Epoch 51/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1207.0496 - mean_absolute_error: 1207.0496 - val_loss: 1092.1243 - val_mean_absolute_error: 1092.1243\n",
      "Epoch 52/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1202.0668 - mean_absolute_error: 1202.0668 - val_loss: 1088.1554 - val_mean_absolute_error: 1088.1554\n",
      "Epoch 53/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1197.3154 - mean_absolute_error: 1197.3154 - val_loss: 1083.5160 - val_mean_absolute_error: 1083.5160\n",
      "Epoch 54/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1192.4203 - mean_absolute_error: 1192.4203 - val_loss: 1079.7968 - val_mean_absolute_error: 1079.7968\n",
      "Epoch 55/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1187.5342 - mean_absolute_error: 1187.5342 - val_loss: 1075.9313 - val_mean_absolute_error: 1075.9313\n",
      "Epoch 56/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1183.0986 - mean_absolute_error: 1183.0986 - val_loss: 1072.5869 - val_mean_absolute_error: 1072.5869\n",
      "Epoch 57/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1178.2245 - mean_absolute_error: 1178.2245 - val_loss: 1067.2013 - val_mean_absolute_error: 1067.2013\n",
      "Epoch 58/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1173.2485 - mean_absolute_error: 1173.2485 - val_loss: 1059.7290 - val_mean_absolute_error: 1059.7290\n",
      "Epoch 59/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1167.1606 - mean_absolute_error: 1167.1606 - val_loss: 1055.0540 - val_mean_absolute_error: 1055.0540\n",
      "Epoch 60/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1162.7480 - mean_absolute_error: 1162.7480 - val_loss: 1050.8956 - val_mean_absolute_error: 1050.8956\n",
      "Epoch 61/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1156.1820 - mean_absolute_error: 1156.1820 - val_loss: 1045.5409 - val_mean_absolute_error: 1045.5409\n",
      "Epoch 62/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1152.1001 - mean_absolute_error: 1152.1001 - val_loss: 1040.6938 - val_mean_absolute_error: 1040.6938\n",
      "Epoch 63/200\n",
      "25/25 [==============================] - 0s 5ms/step - loss: 1145.5293 - mean_absolute_error: 1145.5293 - val_loss: 1042.5526 - val_mean_absolute_error: 1042.5526\n",
      "Epoch 64/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1140.6019 - mean_absolute_error: 1140.6019 - val_loss: 1034.7336 - val_mean_absolute_error: 1034.7336\n",
      "Epoch 65/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1135.1943 - mean_absolute_error: 1135.1943 - val_loss: 1025.9238 - val_mean_absolute_error: 1025.9238\n",
      "Epoch 66/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1130.3048 - mean_absolute_error: 1130.3048 - val_loss: 1020.5139 - val_mean_absolute_error: 1020.5139\n",
      "Epoch 67/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1123.0975 - mean_absolute_error: 1123.0975 - val_loss: 1016.8546 - val_mean_absolute_error: 1016.8546\n",
      "Epoch 68/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1117.5911 - mean_absolute_error: 1117.5911 - val_loss: 1011.5348 - val_mean_absolute_error: 1011.5348\n",
      "Epoch 69/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1111.2983 - mean_absolute_error: 1111.2983 - val_loss: 1006.6462 - val_mean_absolute_error: 1006.6462\n",
      "Epoch 70/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1106.5292 - mean_absolute_error: 1106.5292 - val_loss: 1001.0378 - val_mean_absolute_error: 1001.0378\n",
      "Epoch 71/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1100.0649 - mean_absolute_error: 1100.0649 - val_loss: 996.7982 - val_mean_absolute_error: 996.7982\n",
      "Epoch 72/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1094.3273 - mean_absolute_error: 1094.3273 - val_loss: 988.9373 - val_mean_absolute_error: 988.9373\n",
      "Epoch 73/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1087.4210 - mean_absolute_error: 1087.4210 - val_loss: 982.6157 - val_mean_absolute_error: 982.6157\n",
      "Epoch 74/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1081.9153 - mean_absolute_error: 1081.9153 - val_loss: 972.8780 - val_mean_absolute_error: 972.8780\n",
      "Epoch 75/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1075.8950 - mean_absolute_error: 1075.8950 - val_loss: 964.2417 - val_mean_absolute_error: 964.2417\n",
      "Epoch 76/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1068.7104 - mean_absolute_error: 1068.7104 - val_loss: 958.8185 - val_mean_absolute_error: 958.8185\n",
      "Epoch 77/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1063.3723 - mean_absolute_error: 1063.3723 - val_loss: 951.7866 - val_mean_absolute_error: 951.7866\n",
      "Epoch 78/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1056.2354 - mean_absolute_error: 1056.2354 - val_loss: 944.3379 - val_mean_absolute_error: 944.3379\n",
      "Epoch 79/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1049.7375 - mean_absolute_error: 1049.7375 - val_loss: 940.7164 - val_mean_absolute_error: 940.7164\n",
      "Epoch 80/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1044.6278 - mean_absolute_error: 1044.6278 - val_loss: 938.6345 - val_mean_absolute_error: 938.6345\n",
      "Epoch 81/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 1038.5801 - mean_absolute_error: 1038.5801 - val_loss: 946.3259 - val_mean_absolute_error: 946.3259\n",
      "Epoch 82/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 1033.3174 - mean_absolute_error: 1033.3174 - val_loss: 934.2578 - val_mean_absolute_error: 934.2578\n",
      "Epoch 83/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 1025.6479 - mean_absolute_error: 1025.6479 - val_loss: 927.1202 - val_mean_absolute_error: 927.1202\n",
      "Epoch 84/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 1019.4674 - mean_absolute_error: 1019.4674 - val_loss: 931.6382 - val_mean_absolute_error: 931.6382\n",
      "Epoch 85/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 1013.4731 - mean_absolute_error: 1013.4731 - val_loss: 928.7808 - val_mean_absolute_error: 928.7808\n",
      "Epoch 86/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 1005.8883 - mean_absolute_error: 1005.8883 - val_loss: 922.3447 - val_mean_absolute_error: 922.3447\n",
      "Epoch 87/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 999.7595 - mean_absolute_error: 999.7595 - val_loss: 916.3411 - val_mean_absolute_error: 916.3411\n",
      "Epoch 88/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 991.7804 - mean_absolute_error: 991.7804 - val_loss: 915.6969 - val_mean_absolute_error: 915.6969\n",
      "Epoch 89/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 986.5847 - mean_absolute_error: 986.5847 - val_loss: 911.4858 - val_mean_absolute_error: 911.4858\n",
      "Epoch 90/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 979.6699 - mean_absolute_error: 979.6699 - val_loss: 908.3666 - val_mean_absolute_error: 908.3666\n",
      "Epoch 91/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 972.6831 - mean_absolute_error: 972.6831 - val_loss: 905.0090 - val_mean_absolute_error: 905.0090\n",
      "Epoch 92/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 965.7075 - mean_absolute_error: 965.7075 - val_loss: 896.2441 - val_mean_absolute_error: 896.2441\n",
      "Epoch 93/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 958.6129 - mean_absolute_error: 958.6129 - val_loss: 891.0547 - val_mean_absolute_error: 891.0547\n",
      "Epoch 94/200\n",
      "25/25 [==============================] - 0s 5ms/step - loss: 953.6534 - mean_absolute_error: 953.6534 - val_loss: 887.5588 - val_mean_absolute_error: 887.5588\n",
      "Epoch 95/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 943.6506 - mean_absolute_error: 943.6506 - val_loss: 886.4522 - val_mean_absolute_error: 886.4522\n",
      "Epoch 96/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 935.0286 - mean_absolute_error: 935.0286 - val_loss: 884.8940 - val_mean_absolute_error: 884.8940\n",
      "Epoch 97/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 927.5712 - mean_absolute_error: 927.5712 - val_loss: 879.4086 - val_mean_absolute_error: 879.4086\n",
      "Epoch 98/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 920.3730 - mean_absolute_error: 920.3730 - val_loss: 874.8663 - val_mean_absolute_error: 874.8663\n",
      "Epoch 99/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 917.8689 - mean_absolute_error: 917.8689 - val_loss: 870.0962 - val_mean_absolute_error: 870.0962\n",
      "Epoch 100/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 906.4013 - mean_absolute_error: 906.4013 - val_loss: 863.9559 - val_mean_absolute_error: 863.9559\n",
      "Epoch 101/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 901.2095 - mean_absolute_error: 901.2095 - val_loss: 864.3716 - val_mean_absolute_error: 864.3716\n",
      "Epoch 102/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 894.2258 - mean_absolute_error: 894.2258 - val_loss: 854.3320 - val_mean_absolute_error: 854.3320\n",
      "Epoch 103/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 887.1962 - mean_absolute_error: 887.1962 - val_loss: 852.9985 - val_mean_absolute_error: 852.9985\n",
      "Epoch 104/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 879.8924 - mean_absolute_error: 879.8924 - val_loss: 846.7531 - val_mean_absolute_error: 846.7531\n",
      "Epoch 105/200\n",
      "25/25 [==============================] - 0s 5ms/step - loss: 870.4969 - mean_absolute_error: 870.4969 - val_loss: 844.0094 - val_mean_absolute_error: 844.0094\n",
      "Epoch 106/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 866.0880 - mean_absolute_error: 866.0880 - val_loss: 840.1470 - val_mean_absolute_error: 840.1470\n",
      "Epoch 107/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 863.5740 - mean_absolute_error: 863.5740 - val_loss: 842.5117 - val_mean_absolute_error: 842.5117\n",
      "Epoch 108/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 851.7796 - mean_absolute_error: 851.7796 - val_loss: 837.2562 - val_mean_absolute_error: 837.2562\n",
      "Epoch 109/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 848.5151 - mean_absolute_error: 848.5151 - val_loss: 827.2374 - val_mean_absolute_error: 827.2374\n",
      "Epoch 110/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 843.9180 - mean_absolute_error: 843.9180 - val_loss: 828.7946 - val_mean_absolute_error: 828.7946\n",
      "Epoch 111/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 833.8067 - mean_absolute_error: 833.8067 - val_loss: 825.7820 - val_mean_absolute_error: 825.7820\n",
      "Epoch 112/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 824.5304 - mean_absolute_error: 824.5304 - val_loss: 830.0717 - val_mean_absolute_error: 830.0717\n",
      "Epoch 113/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 819.8845 - mean_absolute_error: 819.8845 - val_loss: 825.2054 - val_mean_absolute_error: 825.2054\n",
      "Epoch 114/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 815.5605 - mean_absolute_error: 815.5605 - val_loss: 821.7745 - val_mean_absolute_error: 821.7745\n",
      "Epoch 115/200\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 806.3268 - mean_absolute_error: 806.3268 - val_loss: 825.3483 - val_mean_absolute_error: 825.3483\n",
      "Epoch 116/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 797.3476 - mean_absolute_error: 797.3476 - val_loss: 820.1169 - val_mean_absolute_error: 820.1169\n",
      "Epoch 117/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 792.4217 - mean_absolute_error: 792.4217 - val_loss: 814.0256 - val_mean_absolute_error: 814.0256\n",
      "Epoch 118/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 788.1371 - mean_absolute_error: 788.1371 - val_loss: 817.1448 - val_mean_absolute_error: 817.1448\n",
      "Epoch 119/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 783.4665 - mean_absolute_error: 783.4665 - val_loss: 812.9813 - val_mean_absolute_error: 812.9813\n",
      "Epoch 120/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 776.0108 - mean_absolute_error: 776.0108 - val_loss: 804.9763 - val_mean_absolute_error: 804.9763\n",
      "Epoch 121/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 772.5162 - mean_absolute_error: 772.5162 - val_loss: 801.9982 - val_mean_absolute_error: 801.9982\n",
      "Epoch 122/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 760.4489 - mean_absolute_error: 760.4489 - val_loss: 801.2132 - val_mean_absolute_error: 801.2132\n",
      "Epoch 123/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 754.8645 - mean_absolute_error: 754.8645 - val_loss: 802.1244 - val_mean_absolute_error: 802.1244\n",
      "Epoch 124/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 751.1809 - mean_absolute_error: 751.1809 - val_loss: 800.5701 - val_mean_absolute_error: 800.5701\n",
      "Epoch 125/200\n",
      "25/25 [==============================] - 0s 4ms/step - loss: 742.9178 - mean_absolute_error: 742.9178 - val_loss: 799.6294 - val_mean_absolute_error: 799.6294\n",
      "Epoch 126/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 744.0930 - mean_absolute_error: 744.0930 - val_loss: 800.8599 - val_mean_absolute_error: 800.8599\n",
      "Epoch 127/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 735.9399 - mean_absolute_error: 735.9399 - val_loss: 794.6922 - val_mean_absolute_error: 794.6922\n",
      "Epoch 128/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 722.7473 - mean_absolute_error: 722.7473 - val_loss: 791.8879 - val_mean_absolute_error: 791.8879\n",
      "Epoch 129/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 720.8008 - mean_absolute_error: 720.8008 - val_loss: 793.2982 - val_mean_absolute_error: 793.2982\n",
      "Epoch 130/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 713.3948 - mean_absolute_error: 713.3948 - val_loss: 788.8817 - val_mean_absolute_error: 788.8817\n",
      "Epoch 131/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 709.4537 - mean_absolute_error: 709.4537 - val_loss: 786.1875 - val_mean_absolute_error: 786.1875\n",
      "Epoch 132/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 703.7637 - mean_absolute_error: 703.7637 - val_loss: 780.3069 - val_mean_absolute_error: 780.3069\n",
      "Epoch 133/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 696.0792 - mean_absolute_error: 696.0792 - val_loss: 780.7902 - val_mean_absolute_error: 780.7902\n",
      "Epoch 134/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 698.4346 - mean_absolute_error: 698.4346 - val_loss: 780.5903 - val_mean_absolute_error: 780.5903\n",
      "Epoch 135/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 686.1921 - mean_absolute_error: 686.1921 - val_loss: 774.2579 - val_mean_absolute_error: 774.2579\n",
      "Epoch 136/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 689.9394 - mean_absolute_error: 689.9394 - val_loss: 773.6865 - val_mean_absolute_error: 773.6865\n",
      "Epoch 137/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 684.6001 - mean_absolute_error: 684.6001 - val_loss: 775.1877 - val_mean_absolute_error: 775.1877\n",
      "Epoch 138/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 676.9717 - mean_absolute_error: 676.9717 - val_loss: 774.4254 - val_mean_absolute_error: 774.4254\n",
      "Epoch 139/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 675.4316 - mean_absolute_error: 675.4316 - val_loss: 773.5552 - val_mean_absolute_error: 773.5552\n",
      "Epoch 140/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 666.4391 - mean_absolute_error: 666.4391 - val_loss: 774.9315 - val_mean_absolute_error: 774.9315\n",
      "Epoch 141/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 658.7793 - mean_absolute_error: 658.7793 - val_loss: 770.1984 - val_mean_absolute_error: 770.1984\n",
      "Epoch 142/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 663.4012 - mean_absolute_error: 663.4012 - val_loss: 765.3767 - val_mean_absolute_error: 765.3767\n",
      "Epoch 143/200\n",
      "25/25 [==============================] - 0s 6ms/step - loss: 652.5810 - mean_absolute_error: 652.5810 - val_loss: 766.2836 - val_mean_absolute_error: 766.2836\n",
      "Epoch 144/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 651.3267 - mean_absolute_error: 651.3267 - val_loss: 767.4441 - val_mean_absolute_error: 767.4441\n",
      "Epoch 145/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 652.8086 - mean_absolute_error: 652.8086 - val_loss: 770.3816 - val_mean_absolute_error: 770.3816\n",
      "Epoch 146/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 651.6414 - mean_absolute_error: 651.6414 - val_loss: 766.6718 - val_mean_absolute_error: 766.6718\n",
      "Epoch 147/200\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 633.9060 - mean_absolute_error: 633.9060 - val_loss: 766.9344 - val_mean_absolute_error: 766.9344\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "callbacks = [EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]\n",
    "\n",
    "# Assuming you have already split your data into X_train, X_val, y_train, y_val\n",
    "history = model.fit(X_train, y_train, epochs=200, batch_size=32,\n",
    "                    validation_data=(X_test, y_test), callbacks=callbacks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/namanmuktha/anaconda3/lib/python3.11/site-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 - 0s - loss: 765.3767 - mean_absolute_error: 765.3767 - 21ms/epoch - 3ms/step\n",
      "Test MSE: [765.376708984375, 765.376708984375]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model on the test data\n",
    "test_loss = model.evaluate(X_test, y_test, verbose=2)\n",
    "print(f\"Test MSE: {test_loss}\")\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 773us/step\n"
     ]
    }
   ],
   "source": [
    "smp=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 521.7351  ],\n",
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       "       [ 743.2646  ],\n",
       "       [ 631.10144 ],\n",
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       "       [1104.8368  ],\n",
       "       [ 568.4483  ],\n",
       "       [1030.6704  ],\n",
       "       [ 614.32074 ],\n",
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       "       [ 687.8883  ],\n",
       "       [1231.4635  ],\n",
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       "       [ 227.25468 ],\n",
       "       [1266.0482  ]], dtype=float32)"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "521    1400.000000\n",
       "737     590.551180\n",
       "740      39.370079\n",
       "660     393.700787\n",
       "411     826.771653\n",
       "          ...     \n",
       "408     354.330708\n",
       "332    8400.000000\n",
       "208     875.000000\n",
       "613     590.551180\n",
       "78     1800.000000\n",
       "Name: PRODUCT_LENGTH, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "# Assuming these objects have been properly defined and trained elsewhere in your code\n",
    "# svd_model, scaler, lemmatizer, stop_words, adding_stemming, model\n",
    "\n",
    "# Save the TruncatedSVD model\n",
    "joblib.dump(svd, 'svd_model.pkl')\n",
    "\n",
    "# Save the scaler\n",
    "joblib.dump(scaler, 'scaler.pkl')\n",
    "\n",
    "# Save the lemmatizer\n",
    "#joblib.dump(lemmatizer, 'lemmatizer.pkl')\n",
    "\n",
    "# Save the stop words set\n",
    "#joblib.dump(stop_words, 'stop_words.pkl')\n",
    "\n",
    "# Save the stemming function (ensure this function is defined in a way that it can be serialized)\n",
    "#joblib.dump(adding_stemming, 'stemming_function.pkl')\n",
    "\n",
    "# Save the trained Keras model\n",
    "model.save('trained_model.h5')  # Note: Use .h5 for Keras models specifically\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['glove_model.pkl']"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(, 'glove_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Can't get attribute 'adding_stemming' on <module '__main__'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[64], line 19\u001b[0m\n\u001b[1;32m     17\u001b[0m lemmatizer \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlemmatizer.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# Load your lemmatizer if you've saved one\u001b[39;00m\n\u001b[1;32m     18\u001b[0m stop_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(joblib\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstop_words.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))  \u001b[38;5;66;03m# Load your set of stop words\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m adding_stemming \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstemming_function.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# Assuming this is a function you've defined and saved\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m#model = joblib.load('trained_model.pkl')  # Load your trained Keras model\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdocument_vector\u001b[39m(words):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/numpy_pickle.py:658\u001b[0m, in \u001b[0;36mload\u001b[0;34m(filename, mmap_mode)\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fobj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    653\u001b[0m                 \u001b[38;5;66;03m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[1;32m    654\u001b[0m                 \u001b[38;5;66;03m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[1;32m    655\u001b[0m                 \u001b[38;5;66;03m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n\u001b[1;32m    656\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m load_compatibility(fobj)\n\u001b[0;32m--> 658\u001b[0m             obj \u001b[38;5;241m=\u001b[39m _unpickle(fobj, filename, mmap_mode)\n\u001b[1;32m    659\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/numpy_pickle.py:577\u001b[0m, in \u001b[0;36m_unpickle\u001b[0;34m(fobj, filename, mmap_mode)\u001b[0m\n\u001b[1;32m    575\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 577\u001b[0m     obj \u001b[38;5;241m=\u001b[39m unpickler\u001b[38;5;241m.\u001b[39mload()\n\u001b[1;32m    578\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m unpickler\u001b[38;5;241m.\u001b[39mcompat_mode:\n\u001b[1;32m    579\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe file \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m has been generated with a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    580\u001b[0m                       \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjoblib version less than 0.10. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    581\u001b[0m                       \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease regenerate this pickle file.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    582\u001b[0m                       \u001b[38;5;241m%\u001b[39m filename,\n\u001b[1;32m    583\u001b[0m                       \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/pickle.py:1213\u001b[0m, in \u001b[0;36m_Unpickler.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1211\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEOFError\u001b[39;00m\n\u001b[1;32m   1212\u001b[0m         \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, bytes_types)\n\u001b[0;32m-> 1213\u001b[0m         dispatch[key[\u001b[38;5;241m0\u001b[39m]](\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _Stop \u001b[38;5;28;01mas\u001b[39;00m stopinst:\n\u001b[1;32m   1215\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m stopinst\u001b[38;5;241m.\u001b[39mvalue\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/pickle.py:1538\u001b[0m, in \u001b[0;36m_Unpickler.load_stack_global\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(name) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mstr\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(module) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mstr\u001b[39m:\n\u001b[1;32m   1537\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UnpicklingError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSTACK_GLOBAL requires str\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1538\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfind_class(module, name))\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/pickle.py:1582\u001b[0m, in \u001b[0;36m_Unpickler.find_class\u001b[0;34m(self, module, name)\u001b[0m\n\u001b[1;32m   1580\u001b[0m \u001b[38;5;28m__import__\u001b[39m(module, level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproto \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[0;32m-> 1582\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _getattribute(sys\u001b[38;5;241m.\u001b[39mmodules[module], name)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1584\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(sys\u001b[38;5;241m.\u001b[39mmodules[module], name)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/pickle.py:331\u001b[0m, in \u001b[0;36m_getattribute\u001b[0;34m(obj, name)\u001b[0m\n\u001b[1;32m    329\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, subpath)\n\u001b[1;32m    330\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m--> 331\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt get attribute \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    332\u001b[0m                              \u001b[38;5;241m.\u001b[39mformat(name, obj)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, parent\n",
      "\u001b[0;31mAttributeError\u001b[0m: Can't get attribute 'adding_stemming' on <module '__main__'>"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import gensim.downloader as api\n",
    "import numpy as np\n",
    "import string\n",
    "import joblib\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the entire model back.\n",
    "restored_model = load_model('my_model.h5')\n",
    "\n",
    "# Load necessary assets\n",
    "glove_model = api.load(\"glove-wiki-gigaword-300\")  # Adjust dimensionality as necessary\n",
    "svd_model = joblib.load('svd_model.pkl')  # Your saved TruncatedSVD model\n",
    "scaler = joblib.load('scaler.pkl')  # Your saved scaler\n",
    "lemmatizer = joblib.load('lemmatizer.pkl')  # Load your lemmatizer if you've saved one\n",
    "stop_words = set(joblib.load('stop_words.pkl'))  # Load your set of stop words\n",
    "adding_stemming = joblib.load('stemming_function.pkl')  # Assuming this is a function you've defined and saved\n",
    "#model = joblib.load('trained_model.pkl')  # Load your trained Keras model\n",
    "\n",
    "def document_vector(words):\n",
    "    valid_words = [word for word in words if word in glove_model]\n",
    "    if not valid_words:\n",
    "        return np.zeros(glove_model.vector_size)\n",
    "    return np.mean(glove_model[valid_words], axis=0)\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = ''.join([c for c in text if c not in string.punctuation])\n",
    "    words = text.split()\n",
    "    words = [word for word in words if word not in stop_words]\n",
    "    words = [lemmatizer.lemmatize(word) for word in words]\n",
    "    words = adding_stemming(words)\n",
    "    return ' '.join(words)\n",
    "\n",
    "# Example preprocessing and vectorization for a new text\n",
    "#new_text = \"Twisted Swirl Vintage Blue Phone Case Compatible with iPhone 13,Orange Fluid Abstract Design Cover for Men Girl Women,Unique Soft TPU Bumper Case Cover\",\"[❤【Compatible mode】:This mobile phone case is only compatible with iPhone 13.It supports wireless charging. The precise cutout allows easy access to the charging port, buttons and camera.,❤【Screen & Camera Protection】: Made of high quality shockproof TPU material that protects against falls,drops ,scratches, bumps and more.The raised screen and camera edges help protect screen and camera from scratches.,❤【Clear Vivid Patterns】 : The Clear and fashionable patterns make your mobile phone look very personalized. Made with an unique printing technique, these patterns won’t rub off, fade or smudge.,❤【Perfect Gifts& Anti-yellow】: Fashionable and unique design and exquisite patterns are a good choice for gifts. High quality printing technology ensures that the pattern will not fade, turn yellow or fall off during use.,❤【After-sale Service】:Worry free after sales. If you have any questions, feel free to contact and we will reply within 24 hours.]\",\"<b>Welcome to our store. We aim to offer unique and special case for you. </b><br> <b>Compatibility</b><br> Compatible with iPhone 13. <br>This case is durable, sturdy and scratch-resistant, perfect for self use or gifts.<br> <b>Features</b><br> ❤Made of soft resilient TPU material, this case is durable and lightweight, easy to take on and off. <br> ❤Raised camera and screen edges provide best protection for phones from falls, scratches or bumps. <br>Designed with four air cushion corners, it perfectly mitigates pressure from drops o falls.<br> ❤Independent buttons and precise cutouts. <br>This case fits buttons, camera, speakers, and ports perfectly.<br> ❤The print is vivid and clear, making your phone look unique and and aesthetic. <br>We use supreme scratch resistant ink, the pattern will not peel over time.<br> <b>PACKAGE LIST</b><br> Including 1 x Phone Case<br>\"\n",
    "new_text = (\"Twisted Swirl Vintage Blue Phone Case Compatible with iPhone 13, Orange Fluid Abstract Design Cover for Men Girl Women, Unique Soft TPU Bumper Case Cover \"\n",
    "            \"[❤【Compatible mode】: This mobile phone case is only compatible with iPhone 13. It supports wireless charging. The precise cutout allows easy access to the charging port, buttons and camera. \"\n",
    "            \"❤【Screen & Camera Protection】: Made of high quality shockproof TPU material that protects against falls, drops, scratches, bumps and more. The raised screen and camera edges help protect screen and camera from scratches. \"\n",
    "            \"❤【Clear Vivid Patterns】: The Clear and fashionable patterns make your mobile phone look very personalized. Made with a unique printing technique, these patterns won’t rub off, fade or smudge. \"\n",
    "            \"❤【Perfect Gifts & Anti-yellow】: Fashionable and unique design and exquisite patterns are a good choice for gifts. High quality printing technology ensures that the pattern will not fade, turn yellow or fall off during use. \"\n",
    "            \"❤【After-sale Service】: Worry free after sales. If you have any questions, feel free to contact and we will reply within 24 hours. \"\n",
    "            \"<b>Welcome to our store. We aim to offer unique and special case for you. </b><br> <b>Compatibility</b><br> Compatible with iPhone 13. <br>This case is durable, sturdy and scratch-resistant, perfect for self use or gifts.<br> \"\n",
    "            \"<b>Features</b><br> ❤Made of soft resilient TPU material, this case is durable and lightweight, easy to take on and off. <br> ❤Raised camera and screen edges provide best protection for phones from falls, scratches or bumps. <br> \"\n",
    "            \"Designed with four air cushion corners, it perfectly mitigates pressure from drops or falls.<br> ❤Independent buttons and precise cutouts. <br>This case fits buttons, camera, speakers, and ports perfectly.<br> \"\n",
    "            \"❤The print is vivid and clear, making your phone look unique and aesthetic. <br>We use supreme scratch resistant ink, the pattern will not peel over time.<br> \"\n",
    "            \"<b>PACKAGE LIST</b><br> Including 1 x Phone Case<br>\")\n",
    "\n",
    "processed_words = preprocess_text(new_text)\n",
    "doc_vec = document_vector(processed_words.split())\n",
    "doc_vec = doc_vec.reshape(1, -1)  # Reshape for a single sample\n",
    "\n",
    "# Apply SVD and scaling\n",
    "reduced_vec = svd_model.transform(doc_vec)\n",
    "scaled_vec = scaler.transform(reduced_vec)\n",
    "\n",
    "# Prediction\n",
    "prediction = restored_model.predict(scaled_vec)\n",
    "print(\"Prediction:\", prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Twisted Swirl Vintage Blue Phone Case Compatible with iPhone 13,Orange Fluid Abstract Design Cover for Men Girl Women,Unique Soft TPU Bumper Case Cover',\n",
       " '[❤【Compatible mode】:This mobile phone case is only compatible with iPhone 13.It supports wireless charging. The precise cutout allows easy access to the charging port, buttons and camera.,❤【Screen & Camera Protection】: Made of high quality shockproof TPU material that protects against falls,drops ,scratches, bumps and more.The raised screen and camera edges help protect screen and camera from scratches.,❤【Clear Vivid Patterns】 : The Clear and fashionable patterns make your mobile phone look very personalized. Made with an unique printing technique, these patterns won’t rub off, fade or smudge.,❤【Perfect Gifts& Anti-yellow】: Fashionable and unique design and exquisite patterns are a good choice for gifts. High quality printing technology ensures that the pattern will not fade, turn yellow or fall off during use.,❤【After-sale Service】:Worry free after sales. If you have any questions, feel free to contact and we will reply within 24 hours.]',\n",
       " '<b>Welcome to our store. We aim to offer unique and special case for you. </b><br> <b>Compatibility</b><br> Compatible with iPhone 13. <br>This case is durable, sturdy and scratch-resistant, perfect for self use or gifts.<br> <b>Features</b><br> ❤Made of soft resilient TPU material, this case is durable and lightweight, easy to take on and off. <br> ❤Raised camera and screen edges provide best protection for phones from falls, scratches or bumps. <br>Designed with four air cushion corners, it perfectly mitigates pressure from drops o falls.<br> ❤Independent buttons and precise cutouts. <br>This case fits buttons, camera, speakers, and ports perfectly.<br> ❤The print is vivid and clear, making your phone look unique and and aesthetic. <br>We use supreme scratch resistant ink, the pattern will not peel over time.<br> <b>PACKAGE LIST</b><br> Including 1 x Phone Case<br>')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Twisted Swirl Vintage Blue Phone Case Compatible with iPhone 13,Orange Fluid Abstract Design Cover for Men Girl Women,Unique Soft TPU Bumper Case Cover\",\"[❤【Compatible mode】:This mobile phone case is only compatible with iPhone 13.It supports wireless charging. The precise cutout allows easy access to the charging port, buttons and camera.,❤【Screen & Camera Protection】: Made of high quality shockproof TPU material that protects against falls,drops ,scratches, bumps and more.The raised screen and camera edges help protect screen and camera from scratches.,❤【Clear Vivid Patterns】 : The Clear and fashionable patterns make your mobile phone look very personalized. Made with an unique printing technique, these patterns won’t rub off, fade or smudge.,❤【Perfect Gifts& Anti-yellow】: Fashionable and unique design and exquisite patterns are a good choice for gifts. High quality printing technology ensures that the pattern will not fade, turn yellow or fall off during use.,❤【After-sale Service】:Worry free after sales. If you have any questions, feel free to contact and we will reply within 24 hours.]\",\"<b>Welcome to our store. We aim to offer unique and special case for you. </b><br> <b>Compatibility</b><br> Compatible with iPhone 13. <br>This case is durable, sturdy and scratch-resistant, perfect for self use or gifts.<br> <b>Features</b><br> ❤Made of soft resilient TPU material, this case is durable and lightweight, easy to take on and off. <br> ❤Raised camera and screen edges provide best protection for phones from falls, scratches or bumps. <br>Designed with four air cushion corners, it perfectly mitigates pressure from drops o falls.<br> ❤Independent buttons and precise cutouts. <br>This case fits buttons, camera, speakers, and ports perfectly.<br> ❤The print is vivid and clear, making your phone look unique and and aesthetic. <br>We use supreme scratch resistant ink, the pattern will not peel over time.<br> <b>PACKAGE LIST</b><br> Including 1 x Phone Case<br>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mhead()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"Caroline's Treasures BB5451GF The Mountain is Waiting Flag Garden Size, Small, Multicolor [The Mountain is Waiting Flag Garden Size BB5451GF Ships from our store in Mobile, AL usually in 24-48 hours. Transit times are usually about 2-3 days.,Double Sided 2 Ply Heavy Weight Garden Flag Printed on Both Sides,This flag is fade resistant and weather proof and measures approximately 11 inches x 15 inches,Made in the USA,Caroline's Treasures products are made in the USA! Make sure to buy all of our products from an authorized US distributor.]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import GPT2Tokenizer, GPT2Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1594019</td>\n",
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       "      <td>950.000000</td>\n",
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       "      <td>2026580</td>\n",
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       "      <td>[hexwell, essential, oil, home, fragrance, oil...</td>\n",
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       "   PRODUCT_ID                                              TITLE  \\\n",
       "2     2765088  [priknik, horn, red, electric, air, horn, comp...   \n",
       "3     1594019  [alishah, womens, cotton, ankle, length, leggi...   \n",
       "5     2152929  [hins, metal, bucket, shape, plant, pot, indoo...   \n",
       "7     2026580  [delavala, self, adhesive, kitchen, backsplash...   \n",
       "9     2998633  [hexwell, essential, oil, home, fragrance, oil...   \n",
       "\n",
       "                                       BULLET_POINTS  \\\n",
       "2  [loud, dual, tone, trumpet, horn, compatible, ...   \n",
       "3  [made, 95cotton, 5, lycra, gives, 4, ways, str...   \n",
       "5  [simple, elegant, great, displaying, indoor, p...   \n",
       "7  [high, quality, pvc, material, kitchen, alumin...   \n",
       "9  [100, pure, natural, essential, oil, fragrance...   \n",
       "\n",
       "                                         DESCRIPTION  PRODUCT_TYPE_ID  \\\n",
       "2  [specifications, color, red, material, alumini...             7537   \n",
       "3  [aishah, womens, lycra, cotton, ankel, legging...             2996   \n",
       "5  [hins, brings, elegant, looking, pot, stand, d...             5725   \n",
       "7  [pstrongaluminum, foil, stickersgood, kitchen,...             6030   \n",
       "9  [transform, home, workplace, hotel, room, pers...             8201   \n",
       "\n",
       "   PRODUCT_LENGTH  \n",
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       "5      950.000000  \n",
       "7      984.251967  \n",
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      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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