main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_12464\\3933052042.py:2: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  book = pd.read_csv(r\"C:\\Users\\ASUS\\OneDrive\\Documents\\GitHub\\Book_Recommendation\\resources\\Books.csv\", encoding='latin-1')\n"
     ]
    }
   ],
   "source": [
    "user = pd.read_csv(r\"C:\\Users\\ASUS\\OneDrive\\Documents\\GitHub\\Book_Recommendation\\Data Preprocessing & EDA\\User.csv\")\n",
    "book = pd.read_csv(r\"C:\\Users\\ASUS\\OneDrive\\Documents\\GitHub\\Book_Recommendation\\resources\\Books.csv\", encoding='latin-1')\n",
    "area = pd.read_csv(r\"C:\\Users\\ASUS\\OneDrive\\Documents\\GitHub\\Book_Recommendation\\resources\\Ratings.csv\", encoding='latin-1')\n",
    "\n",
    "# Adding Pickle File\n",
    "adult = pd.read_pickle('age_adult.pkl')\n",
    "child = pd.read_pickle('age_child.pkl')\n",
    "young = pd.read_pickle('age_young.pkl')\n",
    "other = pd.read_pickle('age_other.pkl')\n",
    "elder = pd.read_pickle('age_elder.pkl')\n",
    "teen = pd.read_pickle('age_teen.pkl')\n",
    "usa = pd.read_pickle('area_usa.pkl')\n",
    "canada = pd.read_pickle('area_canada.pkl')\n",
    "uk = pd.read_pickle('area_uk.pkl')\n",
    "australia = pd.read_pickle('area_australia.pkl')\n",
    "germany = pd.read_pickle('area_germany.pkl')\n",
    "others = pd.read_pickle('area_others.pkl')\n",
    "\n",
    "# New\n",
    "new_adult = pd.read_pickle('new_adult.pkl')\n",
    "new_child = pd.read_pickle('new_children.pkl')\n",
    "new_young = pd.read_pickle('new_youngster.pkl')\n",
    "new_other = pd.read_pickle('new_other.pkl')\n",
    "new_elder = pd.read_pickle('new_elder.pkl')\n",
    "new_teen = pd.read_pickle('new_youngster.pkl')\n",
    "new_usa = pd.read_pickle('new_usa.pkl')\n",
    "new_canada = pd.read_pickle('new_canada.pkl')\n",
    "new_uk = pd.read_pickle('new_uk.pkl')\n",
    "new_spain = pd.read_pickle('new_spain.pkl')\n",
    "new_germany = pd.read_pickle('new_germany.pkl')\n",
    "new_others = pd.read_pickle('new_others.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>User_Id</th>\n",
       "      <th>area</th>\n",
       "      <th>age</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>usa</td>\n",
       "      <td>Youngster</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>others</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>others</td>\n",
       "      <td>Teenager</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>unitedkingdom</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>278855</td>\n",
       "      <td>unitedkingdom</td>\n",
       "      <td>Adult</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278855</th>\n",
       "      <td>278856</td>\n",
       "      <td>canada</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278856</th>\n",
       "      <td>278857</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278857</th>\n",
       "      <td>278858</td>\n",
       "      <td>others</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278858 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_Id           area        age type\n",
       "0             1            usa      other  new\n",
       "1             2            usa  Youngster  new\n",
       "2             3         others      other  new\n",
       "3             4         others   Teenager  new\n",
       "4             5  unitedkingdom      other  new\n",
       "...         ...            ...        ...  ...\n",
       "278853   278854            usa      other  old\n",
       "278854   278855  unitedkingdom      Adult  old\n",
       "278855   278856         canada      other  old\n",
       "278856   278857            usa      other  old\n",
       "278857   278858         others      other  old\n",
       "\n",
       "[278858 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_result(s,x):\n",
    "    ISBN = []\n",
    "    rating = []\n",
    "\n",
    "    for i in s[s['user-2'] == x].loc[:,'user-1']:\n",
    "        for j in area[(area['User-ID'] == i)].index:\n",
    "            k = area.loc[j,'ISBN']\n",
    "            l = area.loc[j,'Book-Rating']\n",
    "            ISBN.append(k)\n",
    "            rating.append(l)\n",
    "\n",
    "    result = pd.DataFrame(\n",
    "        {\n",
    "            'ISBN' : ISBN,\n",
    "            'rating' : rating\n",
    "        }\n",
    "    )\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ISBN(x):\n",
    "    d = []\n",
    "    for i in area[(area['User-ID'] == x)].loc[:,'ISBN'].values:\n",
    "        d.append(i)\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop(a,b):\n",
    "\n",
    "    drop = []\n",
    "\n",
    "    for i in range(len(a)):\n",
    "        for j in b:\n",
    "            c = a.loc[i,'ISBN']\n",
    "            if c == j:\n",
    "                drop.append(i)\n",
    "\n",
    "    return drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fillter_ISBN(x, y):\n",
    "\n",
    "    x.drop(index=y, inplace= True)\n",
    "    x.drop_duplicates(inplace=True)\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_name(x):\n",
    "    if book[book['ISBN'] == x].shape[0] == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return book[book['ISBN'] == x].loc[:,'Book-Title'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_Author(x):\n",
    "    if book[book['ISBN'] == x].shape[0] == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return book[book['ISBN'] == x].loc[:,'Book-Author'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_Publisher(x):\n",
    "    if book[book['ISBN'] == x].shape[0] == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return book[book['ISBN'] == x].loc[:,'Publisher'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_Image(x):\n",
    "    if book[book['ISBN'] == x].shape[0] == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return book[book['ISBN'] == x].loc[:,'Image-URL-L'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predect(similarity,ISBN):\n",
    "\n",
    "    o = create_result(similarity,ISBN)\n",
    "    k = get_ISBN(ISBN)\n",
    "    n = drop(o,k)\n",
    "    e = fillter_ISBN(o,n)\n",
    "    e = e.sort_values(by='rating', ascending= False).head(75)\n",
    "    e['name'] = e['ISBN'].apply(lambda x: set_name(x))\n",
    "    e['auther'] = e['ISBN'].apply(lambda x: set_Author(x))\n",
    "    e['publisher'] = e['ISBN'].apply(lambda x: set_Publisher(x))\n",
    "    e['image'] = e['ISBN'].apply(lambda x: set_Image(x))\n",
    "    e.dropna(inplace=True)\n",
    "\n",
    "    return e.sort_values(by='rating', ascending= False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_detail(x):\n",
    "    area = user[user['User_Id'] == x].loc[:,'area'].values[0]\n",
    "    age = user[user['User_Id'] == x].loc[:,'age'].values[0]\n",
    "    u_type = user[user['User_Id'] == x].loc[:,'type'].values[0]\n",
    "\n",
    "    return area,age,u_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>User_Id</th>\n",
       "      <th>area</th>\n",
       "      <th>age</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>usa</td>\n",
       "      <td>Youngster</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>others</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>others</td>\n",
       "      <td>Teenager</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>unitedkingdom</td>\n",
       "      <td>other</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>278855</td>\n",
       "      <td>unitedkingdom</td>\n",
       "      <td>Adult</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278855</th>\n",
       "      <td>278856</td>\n",
       "      <td>canada</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278856</th>\n",
       "      <td>278857</td>\n",
       "      <td>usa</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278857</th>\n",
       "      <td>278858</td>\n",
       "      <td>others</td>\n",
       "      <td>other</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278858 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_Id           area        age type\n",
       "0             1            usa      other  new\n",
       "1             2            usa  Youngster  new\n",
       "2             3         others      other  new\n",
       "3             4         others   Teenager  new\n",
       "4             5  unitedkingdom      other  new\n",
       "...         ...            ...        ...  ...\n",
       "278853   278854            usa      other  old\n",
       "278854   278855  unitedkingdom      Adult  old\n",
       "278855   278856         canada      other  old\n",
       "278856   278857            usa      other  old\n",
       "278857   278858         others      other  old\n",
       "\n",
       "[278858 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_age(user_id):\n",
    "\n",
    "    age = get_detail(user_id)[1]\n",
    "    type = get_detail(user_id)[2]\n",
    "\n",
    "    try:\n",
    "\n",
    "        if type == 'old':\n",
    "            if age == 'Adult':\n",
    "                df_age = predect(adult,user_id)\n",
    "            elif age == 'other':\n",
    "                df_age = predect(other,user_id)\n",
    "            elif age == 'Youngster':\n",
    "                df_age = predect(young,user_id)\n",
    "            elif age == 'Teenager':\n",
    "                df_age = predect(teen,user_id)\n",
    "            elif age == 'Elder':\n",
    "                df_age = predect(elder,user_id)\n",
    "            elif age == 'Children':\n",
    "                df_age = predect(child,user_id)\n",
    "\n",
    "        elif type == 'new':\n",
    "            if age == 'Adult':\n",
    "                df_age = new_adult\n",
    "            elif age == 'other':\n",
    "                df_age = new_other\n",
    "            elif age == 'Youngster':\n",
    "                df_age = new_young\n",
    "            elif age == 'Teenager':\n",
    "                df_age = new_teen\n",
    "            elif age == 'Elder':\n",
    "                df_age = new_elder\n",
    "            elif age == 'Children':\n",
    "                df_age = new_child\n",
    "\n",
    "    except:\n",
    "        if age == 'Adult':\n",
    "            df_age = new_adult\n",
    "        elif age == 'other':\n",
    "            df_age = new_other\n",
    "        elif age == 'Youngster':\n",
    "            df_age = new_young\n",
    "        elif age == 'Teenager':\n",
    "            df_age = new_teen\n",
    "        elif age == 'Elder':\n",
    "            df_age = new_elder\n",
    "        elif age == 'Children':\n",
    "            df_age = new_child\n",
    "\n",
    "    if df_age.shape[0] == 0:\n",
    "        if age == 'Adult':\n",
    "            df_age = new_adult\n",
    "        elif age == 'other':\n",
    "            df_age = new_other\n",
    "        elif age == 'Youngster':\n",
    "            df_age = new_young\n",
    "        elif age == 'Teenager':\n",
    "            df_age = new_teen\n",
    "        elif age == 'Elder':\n",
    "            df_age = new_elder\n",
    "        elif age == 'Children':\n",
    "            df_age = new_child\n",
    "    return df_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_adult = pd.read_pickle('new_adult.pkl')\n",
    "new_child = pd.read_pickle('new_children.pkl')\n",
    "new_young = pd.read_pickle('new_youngster.pkl')\n",
    "new_other = pd.read_pickle('new_other.pkl')\n",
    "new_elder = pd.read_pickle('new_elder.pkl')\n",
    "new_teen = pd.read_pickle('new_youngster.pkl')\n",
    "new_usa = pd.read_pickle('new_usa.pkl')\n",
    "new_canada = pd.read_pickle('new_canada.pkl')\n",
    "new_uk = pd.read_pickle('new_uk.pkl')\n",
    "new_spain = pd.read_pickle('new_spain.pkl')\n",
    "new_germany = pd.read_pickle('new_germany.pkl')\n",
    "new_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_area(user_id):\n",
    "\n",
    "    area = get_detail(user_id)[0]\n",
    "    type = get_detail(user_id)[2]\n",
    "\n",
    "    if type == 'old':\n",
    "        if area == 'usa':\n",
    "            return predect(usa,user_id)\n",
    "        elif area == 'other':\n",
    "            return predect(others,user_id)\n",
    "        elif area == 'canada':\n",
    "            return predect(canada,user_id)\n",
    "        elif area == 'unitedkingdom':\n",
    "            return predect(uk,user_id)\n",
    "        elif area == 'germany':\n",
    "            return predect(germany,user_id)\n",
    "        elif area == 'australia':\n",
    "            return predect(australia,user_id)\n",
    "\n",
    "    elif type == 'new':\n",
    "        if area == 'usa':\n",
    "            return new_usa\n",
    "        elif area == 'canada':\n",
    "            return new_canada\n",
    "        elif area == 'unitedkingdom':\n",
    "            return new_uk\n",
    "        elif area == 'germany':\n",
    "            return new_germany\n",
    "        elif area == 'australia':\n",
    "            return new_spain\n",
    "        elif area == 'other':\n",
    "            return new_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def if_area_empty(x,user_id):\n",
    "    \n",
    "    area = get_detail(user_id)[0]\n",
    "\n",
    "    if x.shape[0] == 0:\n",
    "        if area == 'usa':\n",
    "            x = new_usa\n",
    "        elif area == 'canada':\n",
    "            x = new_canada\n",
    "        elif area == 'unitedkingdom':\n",
    "            x = new_uk\n",
    "        elif area == 'germany':\n",
    "            x = new_germany\n",
    "        elif area == 'australia':\n",
    "            x = new_spain\n",
    "        elif area == 'other':\n",
    "            x = new_others\n",
    "\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('usa', 'other', 'old')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_detail(278857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ISBN, rating]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_result(usa,278857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user-1</th>\n",
       "      <th>user-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>254</td>\n",
       "      <td>88122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507</td>\n",
       "      <td>37712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>882</td>\n",
       "      <td>149084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1435</td>\n",
       "      <td>264543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733</td>\n",
       "      <td>180917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>277427</td>\n",
       "      <td>224340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>277639</td>\n",
       "      <td>240035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>278137</td>\n",
       "      <td>31040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>278188</td>\n",
       "      <td>218569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>278418</td>\n",
       "      <td>170742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1192 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user-1  user-2\n",
       "0        254   88122\n",
       "1        507   37712\n",
       "2        882  149084\n",
       "3       1435  264543\n",
       "4       1733  180917\n",
       "...      ...     ...\n",
       "1187  277427  224340\n",
       "1188  277639  240035\n",
       "1189  278137   31040\n",
       "1190  278188  218569\n",
       "1191  278418  170742\n",
       "\n",
       "[1192 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user-1</th>\n",
       "      <th>user-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user-1, user-2]\n",
       "Index: []"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa[usa['user-2'] == 278857]"
   ]
  },
  {
   "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",
       "        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>ISBN</th>\n",
       "      <th>rating</th>\n",
       "      <th>name</th>\n",
       "      <th>auther</th>\n",
       "      <th>publisher</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ISBN, rating, name, auther, publisher, image]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predect(usa,278857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "149084 , 88122"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'df_area' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12464\\3831189528.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpred_area\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m152\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mif_area_empty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m152\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12464\\1856843795.py\u001b[0m in \u001b[0;36mpred_area\u001b[1;34m(user_id)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mdf_area\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'df_area' referenced before assignment"
     ]
    }
   ],
   "source": [
    "a = pred_area(152)\n",
    "if_area_empty(a,152)\n",
    "\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Country</th>\n",
       "      <th>auther</th>\n",
       "      <th>publisher</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>971880107</td>\n",
       "      <td>Wild Animus</td>\n",
       "      <td>usa</td>\n",
       "      <td>Rich Shapero</td>\n",
       "      <td>Too Far</td>\n",
       "      <td>http://images.amazon.com/images/P/0971880107.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>316666343</td>\n",
       "      <td>The Lovely Bones: A Novel</td>\n",
       "      <td>adult</td>\n",
       "      <td>Alice Sebold</td>\n",
       "      <td>Little, Brown</td>\n",
       "      <td>http://images.amazon.com/images/P/0316666343.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>059035342X</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "      <td>youngster</td>\n",
       "      <td>J. K. Rowling</td>\n",
       "      <td>Arthur A</td>\n",
       "      <td>http://images.amazon.com/images/P/059035342X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>312979479</td>\n",
       "      <td>And Then There Were None : A Novel</td>\n",
       "      <td>teenager</td>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>St. Martin's Paperbacks</td>\n",
       "      <td>http://images.amazon.com/images/P/0312979479.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>440241073</td>\n",
       "      <td>The Da Vinci Code</td>\n",
       "      <td>elder</td>\n",
       "      <td>DAN BROWN</td>\n",
       "      <td>Doubleday</td>\n",
       "      <td>http://images.amazon.com/images/P/0739307312.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ISBN                                         Book-Title    Country  \\\n",
       "0    971880107                                        Wild Animus       usa    \n",
       "26   316666343                          The Lovely Bones: A Novel      adult   \n",
       "34  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...  youngster   \n",
       "36   312979479                 And Then There Were None : A Novel   teenager   \n",
       "42   440241073                                  The Da Vinci Code     elder    \n",
       "\n",
       "             auther                publisher  \\\n",
       "0      Rich Shapero                  Too Far   \n",
       "26     Alice Sebold            Little, Brown   \n",
       "34    J. K. Rowling                 Arthur A   \n",
       "36  Agatha Christie  St. Martin's Paperbacks   \n",
       "42        DAN BROWN                Doubleday   \n",
       "\n",
       "                                                image  \n",
       "0   http://images.amazon.com/images/P/0971880107.0...  \n",
       "26  http://images.amazon.com/images/P/0316666343.0...  \n",
       "34  http://images.amazon.com/images/P/059035342X.0...  \n",
       "36  http://images.amazon.com/images/P/0312979479.0...  \n",
       "42  http://images.amazon.com/images/P/0739307312.0...  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pred_age(1)\n",
    "\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ISBN                                         Book-Title  \\\n",
       "0  0195153448                                Classical Mythology   \n",
       "1  0002005018                                       Clara Callan   \n",
       "2  0060973129                               Decision in Normandy   \n",
       "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4  0393045218                             The Mummies of Urumchi   \n",
       "\n",
       "            Book-Author Year-Of-Publication                   Publisher  \\\n",
       "0    Mark P. O. Morford                2002     Oxford University Press   \n",
       "1  Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
       "2          Carlo D'Este                1991             HarperPerennial   \n",
       "3      Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
       "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
       "\n",
       "                                         Image-URL-S  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-M  \\\n",
       "0  http://images.amazon.com/images/P/0195153448.0...   \n",
       "1  http://images.amazon.com/images/P/0002005018.0...   \n",
       "2  http://images.amazon.com/images/P/0060973129.0...   \n",
       "3  http://images.amazon.com/images/P/0374157065.0...   \n",
       "4  http://images.amazon.com/images/P/0393045218.0...   \n",
       "\n",
       "                                         Image-URL-L  \n",
       "0  http://images.amazon.com/images/P/0195153448.0...  \n",
       "1  http://images.amazon.com/images/P/0002005018.0...  \n",
       "2  http://images.amazon.com/images/P/0060973129.0...  \n",
       "3  http://images.amazon.com/images/P/0374157065.0...  \n",
       "4  http://images.amazon.com/images/P/0393045218.0...  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "usa                5\n",
       "canada             5\n",
       "united kingdom     5\n",
       "germany            5\n",
       "spain              5\n",
       "adult              5\n",
       "youngster          5\n",
       "teenager           5\n",
       "elder              5\n",
       "children           5\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Country</th>\n",
       "      <th>auther</th>\n",
       "      <th>publisher</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>971880107</td>\n",
       "      <td>Wild Animus</td>\n",
       "      <td>usa</td>\n",
       "      <td>Rich Shapero</td>\n",
       "      <td>Too Far</td>\n",
       "      <td>http://images.amazon.com/images/P/0971880107.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>316666343</td>\n",
       "      <td>The Lovely Bones: A Novel</td>\n",
       "      <td>adult</td>\n",
       "      <td>Alice Sebold</td>\n",
       "      <td>Little, Brown</td>\n",
       "      <td>http://images.amazon.com/images/P/0316666343.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>059035342X</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "      <td>youngster</td>\n",
       "      <td>J. K. Rowling</td>\n",
       "      <td>Arthur A</td>\n",
       "      <td>http://images.amazon.com/images/P/059035342X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>312979479</td>\n",
       "      <td>And Then There Were None : A Novel</td>\n",
       "      <td>teenager</td>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>St. Martin's Paperbacks</td>\n",
       "      <td>http://images.amazon.com/images/P/0312979479.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>440241073</td>\n",
       "      <td>The Da Vinci Code</td>\n",
       "      <td>elder</td>\n",
       "      <td>DAN BROWN</td>\n",
       "      <td>Doubleday</td>\n",
       "      <td>http://images.amazon.com/images/P/0739307312.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ISBN                                         Book-Title    Country  \\\n",
       "0    971880107                                        Wild Animus       usa    \n",
       "26   316666343                          The Lovely Bones: A Novel      adult   \n",
       "34  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...  youngster   \n",
       "36   312979479                 And Then There Were None : A Novel   teenager   \n",
       "42   440241073                                  The Da Vinci Code     elder    \n",
       "\n",
       "             auther                publisher  \\\n",
       "0      Rich Shapero                  Too Far   \n",
       "26     Alice Sebold            Little, Brown   \n",
       "34    J. K. Rowling                 Arthur A   \n",
       "36  Agatha Christie  St. Martin's Paperbacks   \n",
       "42        DAN BROWN                Doubleday   \n",
       "\n",
       "                                                image  \n",
       "0   http://images.amazon.com/images/P/0971880107.0...  \n",
       "26  http://images.amazon.com/images/P/0316666343.0...  \n",
       "34  http://images.amazon.com/images/P/059035342X.0...  \n",
       "36  http://images.amazon.com/images/P/0312979479.0...  \n",
       "42  http://images.amazon.com/images/P/0739307312.0...  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_other = new.iloc[[0,26,34,36,42],:]\n",
    "\n",
    "new_others"
   ]
  }
 ],
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