Cuisine Recomendation System.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "738919b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "import numpy as np \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import seaborn as sns \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6819212",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"cuis.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cef066e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Area code</th>\n",
       "      <th>Location</th>\n",
       "      <th>Gender</th>\n",
       "      <th>YOB</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Activity</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Food Rating</th>\n",
       "      <th>Service Rating</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Often A S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>Upper East Side,NY</td>\n",
       "      <td>Female</td>\n",
       "      <td>2006</td>\n",
       "      <td>Single</td>\n",
       "      <td>Professional</td>\n",
       "      <td>3</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Never</td>\n",
       "      <td>Never</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>123</td>\n",
       "      <td>St. George,NY</td>\n",
       "      <td>Female</td>\n",
       "      <td>1991</td>\n",
       "      <td>Married</td>\n",
       "      <td>Student</td>\n",
       "      <td>3</td>\n",
       "      <td>Indian</td>\n",
       "      <td>Never</td>\n",
       "      <td>Socially</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>Upper West Side,NY</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977</td>\n",
       "      <td>Single</td>\n",
       "      <td>Student</td>\n",
       "      <td>5</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>Often</td>\n",
       "      <td>Often</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "      <td>Upper East Side,NY</td>\n",
       "      <td>Female</td>\n",
       "      <td>1956</td>\n",
       "      <td>Married</td>\n",
       "      <td>Professional</td>\n",
       "      <td>5</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Never</td>\n",
       "      <td>Socially</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>129</td>\n",
       "      <td>Central Park,NY</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997</td>\n",
       "      <td>Single</td>\n",
       "      <td>Student</td>\n",
       "      <td>4</td>\n",
       "      <td>Filipino</td>\n",
       "      <td>Socially</td>\n",
       "      <td>Never</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User ID  Area code            Location  Gender   YOB Marital Status  \\\n",
       "0        1        153  Upper East Side,NY  Female  2006         Single   \n",
       "1        2        123       St. George,NY  Female  1991        Married   \n",
       "2        3        122  Upper West Side,NY    Male  1977         Single   \n",
       "3        4        153  Upper East Side,NY  Female  1956        Married   \n",
       "4        5        129     Central Park,NY    Male  1997         Single   \n",
       "\n",
       "       Activity  Budget  Cuisines  Alcohol     Smoker  Food Rating  \\\n",
       "0  Professional       3  Japanese     Never     Never            5   \n",
       "1       Student       3    Indian     Never  Socially            1   \n",
       "2       Student       5   Seafood     Often     Often            5   \n",
       "3  Professional       5  Japanese     Never  Socially            3   \n",
       "4       Student       4  Filipino  Socially     Never            2   \n",
       "\n",
       "   Service Rating  Overall Rating Often A S  \n",
       "0               4             4.5        No  \n",
       "1               1             1.0        No  \n",
       "2               5             5.0       Yes  \n",
       "3               1             2.0        No  \n",
       "4               4             3.0        No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdc2bcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User ID           0\n",
       "Area code         0\n",
       "Location          0\n",
       "Gender            0\n",
       "YOB               0\n",
       "Marital Status    0\n",
       "Activity          0\n",
       "Budget            0\n",
       "Cuisines          0\n",
       "Alcohol           0\n",
       "Smoker            0\n",
       "Food Rating       0\n",
       "Service Rating    0\n",
       "Overall Rating    0\n",
       "Often A S         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2128179b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:,[0,6,7,8,11,]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9662ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Activity</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Food Rating</th>\n",
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       "      <td>Japanese</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Student</td>\n",
       "      <td>3</td>\n",
       "      <td>Indian</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Student</td>\n",
       "      <td>5</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Professional</td>\n",
       "      <td>5</td>\n",
       "      <td>Japanese</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>Student</td>\n",
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       "      <td>Filipino</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   User ID      Activity  Budget  Cuisines  Food Rating\n",
       "0        1  Professional       3  Japanese            5\n",
       "1        2       Student       3    Indian            1\n",
       "2        3       Student       5   Seafood            5\n",
       "3        4  Professional       5  Japanese            3\n",
       "4        5       Student       4  Filipino            2"
      ]
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d4cfdaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Japanese', 'Indian', 'Seafood', 'Filipino', 'Italian', 'Chinese',\n",
       "       'French'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Cuisines\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0548f88a",
   "metadata": {},
   "source": [
    "# budget Based Recommendation system "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95134c32",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>User ID</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>191</th>\n",
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       "      <th>199</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuisines</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chinese</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filipino</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seafood</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "User ID   1    2    3    4    5    6    7    8    9    10   ...  191  192  \\\n",
       "Cuisines                                                    ...             \n",
       "Chinese   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  4.0  ...  0.0  0.0   \n",
       "Filipino  0.0  0.0  0.0  0.0  4.0  0.0  0.0  3.0  0.0  0.0  ...  0.0  0.0   \n",
       "French    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  3.0   \n",
       "Indian    0.0  3.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  ...  4.0  0.0   \n",
       "Italian   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  5.0  0.0  ...  0.0  0.0   \n",
       "Japanese  3.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "Seafood   0.0  0.0  5.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "User ID   193  194  195  196  197  198  199  200  \n",
       "Cuisines                                          \n",
       "Chinese   0.0  0.0  0.0  0.0  4.0  0.0  0.0  0.0  \n",
       "Filipino  0.0  5.0  0.0  0.0  0.0  0.0  3.0  0.0  \n",
       "French    5.0  0.0  0.0  4.0  0.0  0.0  0.0  4.0  \n",
       "Indian    0.0  0.0  3.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Italian   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Japanese  0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  \n",
       "Seafood   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[7 rows x 200 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create a pivot table to summarize sales by date and product\n",
    "pt = pd.pivot_table(df, values= 'Budget', index='Cuisines', columns='User ID')\n",
    "\n",
    "pt.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24f6f50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in pt.iterrows():\n",
    "    # Loop through each column in the row\n",
    "    for column in pt.columns:\n",
    "        # Check if the value is NaN\n",
    "        if pd.isna(row[column]):\n",
    "            # Replace NaN with a specific value (e.g., 0)\n",
    "            pt.at[index, column] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f5bf2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User ID\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "5      0\n",
       "      ..\n",
       "196    0\n",
       "197    0\n",
       "198    0\n",
       "199    0\n",
       "200    0\n",
       "Length: 200, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0ac56fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "cosine_sim = cosine_similarity(pt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37abeaaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(Cuisines) :\n",
    "    \n",
    "    index = np.where(pt.index == Cuisines)[0][0]\n",
    "    \n",
    "    distances   = sorted(list(enumerate(cosine_sim[0])),key = lambda  x:x[1], reverse = True )\n",
    "    \n",
    "    for i in distances :\n",
    "        print(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb98e7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1.0000000000000002)\n",
      "(1, 0.0)\n",
      "(2, 0.0)\n",
      "(3, 0.0)\n",
      "(4, 0.0)\n",
      "(5, 0.0)\n",
      "(6, 0.0)\n"
     ]
    }
   ],
   "source": [
    "recommend('Chinese')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536d7477",
   "metadata": {},
   "source": [
    "# Rating Based Recommendation system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e94b2744",
   "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>User ID</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuisines</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chinese</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filipino</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seafood</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "User ID   1    2    3    4    5    6    7    8    9    10   ...  191  192  \\\n",
       "Cuisines                                                    ...             \n",
       "Chinese   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  5.0  ...  0.0  0.0   \n",
       "Filipino  0.0  0.0  0.0  0.0  2.0  0.0  0.0  5.0  0.0  0.0  ...  0.0  0.0   \n",
       "French    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  3.0   \n",
       "Indian    0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  2.0  0.0   \n",
       "Italian   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  0.0  ...  0.0  0.0   \n",
       "Japanese  5.0  0.0  0.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "Seafood   0.0  0.0  5.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "User ID   193  194  195  196  197  198  199  200  \n",
       "Cuisines                                          \n",
       "Chinese   0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "Filipino  0.0  1.0  0.0  0.0  0.0  0.0  3.0  0.0  \n",
       "French    5.0  0.0  0.0  1.0  0.0  0.0  0.0  3.0  \n",
       "Indian    0.0  0.0  2.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Italian   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "Japanese  0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  \n",
       "Seafood   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[7 rows x 200 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create a pivot table to summarize sales by date and product\n",
    "pt = pd.pivot_table(df, values= 'Food Rating', index='Cuisines', columns='User ID')\n",
    "\n",
    "pt.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "922094fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in pt.iterrows():\n",
    "    # Loop through each column in the row\n",
    "    for column in pt.columns:\n",
    "        # Check if the value is NaN\n",
    "        if pd.isna(row[column]):\n",
    "            # Replace NaN with a specific value (e.g., 0)\n",
    "            pt.at[index, column] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0abffc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reconrate(Cuisines) :\n",
    "    \n",
    "    index = np.where(pt.index == Cuisines)[0][0]\n",
    "    \n",
    "    distances   = sorted(list(enumerate(cosine_sim[0])),key = lambda  x:x[1], reverse = True )\n",
    "    \n",
    "    print(distances[0:6])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80c47775",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1.0000000000000002), (1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "reconrate(\"Indian\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a723646",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pt,open('pt.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec2df204",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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