Recommendation system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the dataset\n",
    "df = pd.read_csv(\"Pid-rating.csv\")\n",
    "df1 = pd.read_csv(\"Descriptions.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A39HTATAQ9V7YF</td>\n",
       "      <td>0205616461</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1369699200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3JM6GV9MNOF9X</td>\n",
       "      <td>0558925278</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1355443200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1Z513UWSAAO0F</td>\n",
       "      <td>0558925278</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1404691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1WMRR494NWEWV</td>\n",
       "      <td>0733001998</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1382572800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3IAAVS479H7M7</td>\n",
       "      <td>0737104473</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1274227200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           UserId   ProductId  Rating   Timestamp\n",
       "0  A39HTATAQ9V7YF  0205616461     5.0  1369699200\n",
       "1  A3JM6GV9MNOF9X  0558925278     3.0  1355443200\n",
       "2  A1Z513UWSAAO0F  0558925278     5.0  1404691200\n",
       "3  A1WMRR494NWEWV  0733001998     4.0  1382572800\n",
       "4  A3IAAVS479H7M7  0737104473     1.0  1274227200"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_uid</th>\n",
       "      <th>product_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Not only do angles make joints stronger, they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>BEHR Premium Textured DECKOVER is an innovativ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>Classic architecture meets contemporary design...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>The Grape Solar 265-Watt Polycrystalline PV So...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>Update your bathroom with the Delta Vero Singl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_uid                                product_description\n",
       "0       100001  Not only do angles make joints stronger, they ...\n",
       "1       100002  BEHR Premium Textured DECKOVER is an innovativ...\n",
       "2       100003  Classic architecture meets contemporary design...\n",
       "3       100004  The Grape Solar 265-Watt Polycrystalline PV So...\n",
       "4       100005  Update your bathroom with the Delta Vero Singl..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserId       0\n",
       "ProductId    0\n",
       "Rating       0\n",
       "Timestamp    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_uid            0\n",
       "product_description    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df, col, title):    \n",
    "    f, ax = plt.subplots(figsize=(15, 8))\n",
    "    sns.set(style=\"darkgrid\")\n",
    "    x=df[col].value_counts()\n",
    "    x=x.reset_index()\n",
    "    x[col]=100*x[col]/sum(x[col])\n",
    "    x=x.sort_values(col)\n",
    "    x=x[x[col]>1]\n",
    "    ax = sns.barplot(x=\"index\", y=col, data=x,orient=\"v\")\n",
    "    plt.xticks(rotation='vertical')\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Percentage')\n",
    "    plt.xlabel(col)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(df,'Rating','Rating Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A3KEZLJ59C1JVH    389\n",
       "A281NPSIMI1C2R    336\n",
       "A3M174IC0VXOS2    326\n",
       "A2V5R832QCSOMX    278\n",
       "A3LJLRIZL38GG3    276\n",
       "                 ... \n",
       "A2EJR2QNZHRQ15      1\n",
       "A1OUYY86MJZNWN      1\n",
       "A2JG9LI3GXDUVD      1\n",
       "A1AB75VRUEUNCH      1\n",
       "A183ZGMBC4WWYQ      1\n",
       "Name: UserId, Length: 1210271, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"UserId\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B001MA0QY2    7533\n",
       "B0009V1YR8    2869\n",
       "B0043OYFKU    2477\n",
       "B0000YUXI0    2143\n",
       "B003V265QW    2088\n",
       "              ... \n",
       "B0081UAQTQ       1\n",
       "B0080L5D8K       1\n",
       "B000MMLPHS       1\n",
       "B00ASFX2NE       1\n",
       "B002VECIYE       1\n",
       "Name: ProductId, Length: 249274, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ProductId\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_sold_product = pd.DataFrame(df.groupby(\"ProductId\")[\"Rating\"].count() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0205616461</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0733001998</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0737104473</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0762451459</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00LORWRJA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00LOS7MEE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00LP2YB8E</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00LPVG6V0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00LU0LTOU</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249274 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating\n",
       "ProductId         \n",
       "0205616461       1\n",
       "0558925278       2\n",
       "0733001998       1\n",
       "0737104473       1\n",
       "0762451459       1\n",
       "...            ...\n",
       "B00LORWRJA       1\n",
       "B00LOS7MEE       1\n",
       "B00LP2YB8E       1\n",
       "B00LPVG6V0       1\n",
       "B00LU0LTOU       1\n",
       "\n",
       "[249274 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_sold_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "product = most_sold_product.sort_values(\"Rating\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B001MA0QY2</th>\n",
       "      <td>7533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B0009V1YR8</th>\n",
       "      <td>2869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B0043OYFKU</th>\n",
       "      <td>2477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B0000YUXI0</th>\n",
       "      <td>2143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B003V265QW</th>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating\n",
       "ProductId         \n",
       "B001MA0QY2    7533\n",
       "B0009V1YR8    2869\n",
       "B0043OYFKU    2477\n",
       "B0000YUXI0    2143\n",
       "B003V265QW    2088"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24124096c08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "product.head(10).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#product B001MA0QY2 is the most sold product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART-2\n",
    "Collabarative Filtering system will be used in order to identify the patterns based on the purchase of different products by different users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "utility_matrix = new_df.pivot_table(values = \"Rating\",index = \"UserId\",columns='ProductId',fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "</table>\n",
       "<p>9697 rows × 886 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ProductId              0205616461  0558925278  0733001998  0737104473  \\\n",
       "UserId                                                                  \n",
       "A00205921JHJK5X9LNP42           0           0           0           0   \n",
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       "...                           ...         ...         ...         ...   \n",
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       "\n",
       "ProductId              0762451459  1304139212  1304139220  130414089X  \\\n",
       "UserId                                                                  \n",
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       "\n",
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       "UserId                                         ...                           \n",
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       "...                           ...         ...  ...         ...         ...   \n",
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       "\n",
       "ProductId              B000052YPG  B000052YPH  B000052YPM  B000052YPU  \\\n",
       "UserId                                                                  \n",
       "A00205921JHJK5X9LNP42           0           0           0           0   \n",
       "A024581134CV80ZBLIZTZ           0           0           0           0   \n",
       "A03056581JJIOL5FSKJY7           0           0           0           0   \n",
       "A03099101ZRK4K607JVHH           0           0           0           0   \n",
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       "...                           ...         ...         ...         ...   \n",
       "AZZHJZP4GQPPZ                   0           0           0           0   \n",
       "AZZNK89PXD006                   0           0           0           0   \n",
       "AZZOFVMQC0BJG                   0           0           0           0   \n",
       "AZZQXL8VDCFTV                   0           0           0           0   \n",
       "AZZTJQ7CQZUD8                   0           0           0           0   \n",
       "\n",
       "ProductId              B000052YPV  B000052YPY  B000052YQ0  B000052YQ2  \n",
       "UserId                                                                 \n",
       "A00205921JHJK5X9LNP42           0           0           0           0  \n",
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       "...                           ...         ...         ...         ...  \n",
       "AZZHJZP4GQPPZ                   0           0           0           0  \n",
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       "\n",
       "[9697 rows x 886 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0737104473</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0762451459</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9697 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "UserId      A00205921JHJK5X9LNP42  A024581134CV80ZBLIZTZ  \\\n",
       "ProductId                                                  \n",
       "0205616461                      0                      0   \n",
       "0558925278                      0                      0   \n",
       "0733001998                      0                      0   \n",
       "0737104473                      0                      0   \n",
       "0762451459                      0                      0   \n",
       "\n",
       "UserId      A03056581JJIOL5FSKJY7  A03099101ZRK4K607JVHH  \\\n",
       "ProductId                                                  \n",
       "0205616461                      0                      0   \n",
       "0558925278                      0                      0   \n",
       "0733001998                      0                      0   \n",
       "0737104473                      0                      0   \n",
       "0762451459                      0                      0   \n",
       "\n",
       "UserId      A0505229A7NSH3FRXRR4  A05492663T95KW63BR75K  \\\n",
       "ProductId                                                 \n",
       "0205616461                     0                      0   \n",
       "0558925278                     0                      0   \n",
       "0733001998                     0                      0   \n",
       "0737104473                     0                      0   \n",
       "0762451459                     0                      0   \n",
       "\n",
       "UserId      A059547920Q3LZVFHLPI3  A07410232KYRFR25CIUGJ  \\\n",
       "ProductId                                                  \n",
       "0205616461                      0                      0   \n",
       "0558925278                      0                      0   \n",
       "0733001998                      0                      0   \n",
       "0737104473                      0                      0   \n",
       "0762451459                      0                      0   \n",
       "\n",
       "UserId      A082796624UNM47DSAI6K  A0864963DOAY7LXGS5I6  ...  AZW1HXXYAC15B  \\\n",
       "ProductId                                                ...                  \n",
       "0205616461                      0                     0  ...              0   \n",
       "0558925278                      0                     0  ...              0   \n",
       "0733001998                      0                     0  ...              0   \n",
       "0737104473                      0                     0  ...              0   \n",
       "0762451459                      0                     0  ...              0   \n",
       "\n",
       "UserId      AZWRTJPN7NXT  AZWTXHXZXFAYP  AZYQEFB9Y5N22  AZZHB6U54UDYW  \\\n",
       "ProductId                                                               \n",
       "0205616461             0              0              0              0   \n",
       "0558925278             0              0              0              0   \n",
       "0733001998             0              0              0              0   \n",
       "0737104473             0              0              0              0   \n",
       "0762451459             0              0              0              0   \n",
       "\n",
       "UserId      AZZHJZP4GQPPZ  AZZNK89PXD006  AZZOFVMQC0BJG  AZZQXL8VDCFTV  \\\n",
       "ProductId                                                                \n",
       "0205616461              0              0              0              0   \n",
       "0558925278              0              0              0              0   \n",
       "0733001998              0              0              0              0   \n",
       "0737104473              0              0              0              0   \n",
       "0762451459              0              0              0              0   \n",
       "\n",
       "UserId      AZZTJQ7CQZUD8  \n",
       "ProductId                  \n",
       "0205616461              0  \n",
       "0558925278              0  \n",
       "0733001998              0  \n",
       "0737104473              0  \n",
       "0762451459              0  \n",
       "\n",
       "[5 rows x 9697 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = utility_matrix.T\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposing of Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.46841084e-15,  3.37946060e-14,  5.08511844e-14, ...,\n",
       "         7.38433828e-13,  2.19168268e-12, -7.42153787e-13],\n",
       "       [ 3.05473154e-14, -4.23276176e-13,  4.15544868e-13, ...,\n",
       "        -7.85917906e-11, -4.08931153e-12, -2.96053624e-13],\n",
       "       [-3.93941409e-16,  1.23032391e-14,  1.02512895e-14, ...,\n",
       "         4.16551743e-14,  4.21985219e-13, -3.06383703e-13],\n",
       "       ...,\n",
       "       [ 3.20312126e-06,  2.27949395e-01, -1.04112964e-02, ...,\n",
       "        -4.94001981e-02,  9.79251352e-04,  2.01347455e-03],\n",
       "       [-6.83924837e-06,  7.47554229e-04,  3.40962360e-06, ...,\n",
       "        -1.54151160e-04, -9.90909860e-03, -2.62399432e-03],\n",
       "       [ 3.58071940e-05,  4.16768422e-01,  3.65527137e-01, ...,\n",
       "         1.88270819e-01,  3.35495345e-01, -2.55345912e-01]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVD = TruncatedSVD(n_components=12)\n",
    "decomposition = SVD.fit_transform(X)\n",
    "decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(886, 886)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = np.corrcoef(decomposition)\n",
    "corr_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = \"0205616461\"\n",
    "    \n",
    "product_name = list(X.index)\n",
    "product_ID = product_name.index(i)\n",
    "product_ID    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_product_ID = corr_matrix[product_ID] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0733001998', '1304139212', '1304139220', '130414089X', '130414643X']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For recommending\n",
    "recommend = list(X.index[corr_product_ID > 0.1])\n",
    "recommend.remove(i)\n",
    "recommend[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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_uid</th>\n",
       "      <th>product_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Not only do angles make joints stronger, they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>BEHR Premium Textured DECKOVER is an innovativ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>Classic architecture meets contemporary design...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>The Grape Solar 265-Watt Polycrystalline PV So...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>Update your bathroom with the Delta Vero Singl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_uid                                product_description\n",
       "0       100001  Not only do angles make joints stronger, they ...\n",
       "1       100002  BEHR Premium Textured DECKOVER is an innovativ...\n",
       "2       100003  Classic architecture meets contemporary design...\n",
       "3       100004  The Grape Solar 265-Watt Polycrystalline PV So...\n",
       "4       100005  Update your bathroom with the Delta Vero Singl..."
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptions1 = df1.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Not only do angles make joints stronger, they ...\n",
       "1    BEHR Premium Textured DECKOVER is an innovativ...\n",
       "2    Classic architecture meets contemporary design...\n",
       "3    The Grape Solar 265-Watt Polycrystalline PV So...\n",
       "4    Update your bathroom with the Delta Vero Singl...\n",
       "5    Achieving delicious results is almost effortle...\n",
       "6    The Quantum Adjustable 2-Light LED Black Emerg...\n",
       "7    The Teks #10 x 1-1/2 in. Zinc-Plated Steel Was...\n",
       "8    Get the House of Fara 3/4 in. x 3 in. x 8 ft. ...\n",
       "9    Valley View Industries Metal Stakes (4-Pack) a...\n",
       "Name: product_description, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions1[\"product_description\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words = \"english\")\n",
    "X1 = vectorizer.fit_transform(descriptions1[\"product_description\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Elbow method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "   wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting Kmeans into the data set\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "X=X1\n",
    "\n",
    "kmeans = KMeans(n_clusters =10 ,init='k-means++')\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " ft\n",
      " use\n",
      " easy\n",
      " home\n",
      " wall\n",
      " used\n",
      " wood\n",
      " installation\n",
      " design\n",
      " product\n",
      "Cluster 1:\n",
      " light\n",
      " watt\n",
      " bulb\n",
      " energy\n",
      " led\n",
      " lighting\n",
      " bulbs\n",
      " fan\n",
      " white\n",
      " incandescent\n",
      "Cluster 2:\n",
      " window\n",
      " frame\n",
      " patio\n",
      " fabric\n",
      " table\n",
      " outdoor\n",
      " chairs\n",
      " weather\n",
      " cushions\n",
      " dining\n",
      "Cluster 3:\n",
      " water\n",
      " shower\n",
      " faucet\n",
      " valve\n",
      " toilet\n",
      " sink\n",
      " tank\n",
      " heater\n",
      " hot\n",
      " spray\n",
      "Cluster 4:\n",
      " door\n",
      " doors\n",
      " cabinet\n",
      " vanity\n",
      " glass\n",
      " wood\n",
      " hardware\n",
      " adjustable\n",
      " storage\n",
      " easy\n",
      "Cluster 5:\n",
      " paint\n",
      " rug\n",
      " vary\n",
      " colors\n",
      " ri\n",
      " fee\n",
      " mn\n",
      " ct\n",
      " ca\n",
      " states\n",
      "Cluster 6:\n",
      " steel\n",
      " cutting\n",
      " use\n",
      " blade\n",
      " tool\n",
      " easy\n",
      " handle\n",
      " duty\n",
      " power\n",
      " metal\n",
      "Cluster 7:\n",
      " battery\n",
      " volt\n",
      " lithium\n",
      " ion\n",
      " batteries\n",
      " power\n",
      " charger\n",
      " cordless\n",
      " tool\n",
      " driver\n",
      "Cluster 8:\n",
      " cooking\n",
      " oven\n",
      " cu\n",
      " air\n",
      " temperature\n",
      " freezer\n",
      " cycle\n",
      " ft\n",
      " food\n",
      " burner\n",
      "Cluster 9:\n",
      " tile\n",
      " indoor\n",
      " thinset\n",
      " recommended\n",
      " frost\n",
      " commercial\n",
      " residential\n",
      " wall\n",
      " pieces\n",
      " piece\n"
     ]
    }
   ],
   "source": [
    "true_k = 10\n",
    "\n",
    "model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)\n",
    "model.fit(X1)\n",
    "\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = model.cluster_centers_.argsort()[:, ::-1]  #sorting of centroids\n",
    "terms = vectorizer.get_feature_names()      #vectorized\n",
    "for i in range(true_k):    #for looping through the clusters\n",
    "    print(\"Cluster %d:\" % i),    \n",
    "    for ind in order_centroids[i, :10]:    #the placement through the centroids i.e the item we want to display\n",
    "        print(' %s' % terms[ind]),\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster ID:\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cluster ID:\")\n",
    "Y = vectorizer.transform([\"tile\"])\n",
    "prediction = model.predict(Y)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster ID:\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cluster ID:\")\n",
    "Y = vectorizer.transform([\"bulb\"])\n",
    "prediction = model.predict(Y)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster ID:\n",
      "[3]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cluster ID:\")\n",
    "Y = vectorizer.transform([\"water\"])\n",
    "prediction = model.predict(Y)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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