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Recommender Systems - The Fundamentals.ipynb
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Recommender Systems - The Fundamentals.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Data\n",
"\n",
"The data consists of three tables: ratings, books info, and users info."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"b'Skipping line 6452: expected 8 fields, saw 9\\nSkipping line 43667: expected 8 fields, saw 10\\nSkipping line 51751: expected 8 fields, saw 9\\n'\n",
"b'Skipping line 92038: expected 8 fields, saw 9\\nSkipping line 104319: expected 8 fields, saw 9\\nSkipping line 121768: expected 8 fields, saw 9\\n'\n",
"b'Skipping line 144058: expected 8 fields, saw 9\\nSkipping line 150789: expected 8 fields, saw 9\\nSkipping line 157128: expected 8 fields, saw 9\\nSkipping line 180189: expected 8 fields, saw 9\\nSkipping line 185738: expected 8 fields, saw 9\\n'\n",
"b'Skipping line 209388: expected 8 fields, saw 9\\nSkipping line 220626: expected 8 fields, saw 9\\nSkipping line 227933: expected 8 fields, saw 11\\nSkipping line 228957: expected 8 fields, saw 10\\nSkipping line 245933: expected 8 fields, saw 9\\nSkipping line 251296: expected 8 fields, saw 9\\nSkipping line 259941: expected 8 fields, saw 9\\nSkipping line 261529: expected 8 fields, saw 9\\n'\n",
"C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
"books.columns = ['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
"users = pd.read_csv('BX-Users.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
"users.columns = ['userID', 'Location', 'Age']\n",
"ratings = pd.read_csv('BX-Book-Ratings.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
"ratings.columns = ['userID', 'ISBN', 'bookRating']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ratings Data\n",
"\n",
"The ratings data set provides a list of ratings that users have given to books. It includes 1,149,780 records and 3 fields: userID, ISBN, and rating."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1149780, 3)\n",
"['userID', 'ISBN', 'bookRating']\n"
]
}
],
"source": [
"print(ratings.shape)\n",
"print(list(ratings.columns))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>userID</th>\n",
" <th>ISBN</th>\n",
" <th>bookRating</th>\n",
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"text/plain": [
" userID ISBN bookRating\n",
"0 276725 034545104X 0\n",
"1 276726 0155061224 5\n",
"2 276727 0446520802 0\n",
"3 276729 052165615X 3\n",
"4 276729 0521795028 6"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ratings.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ratings Distribution"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RsVHTbeNh9P9VYEGL8sOBo4ETgb1ynRmSnpl8Imk8MAMI0ozLY4FDgWO62ZaZmXVeO2G2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\nmRXPYWZmZsX7Px9/UWbcNSzhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x141bb3b07f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rc(\"font\", size=15)\n",
"ratings.bookRating.value_counts(sort=False).plot(kind='bar')\n",
"plt.title('Rating Distribution\\n')\n",
"plt.xlabel('Rating')\n",
"plt.ylabel('Count')\n",
"plt.savefig('system1.png', bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Books dataset\n",
"\n",
"This dataset provides books details. It includes 271360 records and 8 fields: ISBN, book title, book author, publisher and so on."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(271360, 8)\n",
"['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n"
]
}
],
"source": [
"print(books.shape)\n",
"print(list(books.columns))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>bookTitle</th>\n",
" <th>bookAuthor</th>\n",
" <th>yearOfPublication</th>\n",
" <th>publisher</th>\n",
" <th>imageUrlS</th>\n",
" <th>imageUrlM</th>\n",
" <th>imageUrlL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <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; 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 bookTitle \\\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",
" bookAuthor yearOfPublication 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 & Company \n",
"\n",
" imageUrlS \\\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",
" imageUrlM \\\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",
" imageUrlL \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": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"books.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Users dataset \n",
"\n",
"This dataset provides the user demographic information. It includes 278858 records and 3 fields: user id, location and age."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(278858, 3)\n",
"['userID', 'Location', 'Age']\n"
]
}
],
"source": [
"print(users.shape)\n",
"print(list(users.columns))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>Location</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>nyc, new york, usa</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>stockton, california, usa</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>moscow, yukon territory, russia</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>porto, v.n.gaia, portugal</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>farnborough, hants, united kingdom</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" userID Location Age\n",
"0 1 nyc, new york, usa NaN\n",
"1 2 stockton, california, usa 18.0\n",
"2 3 moscow, yukon territory, russia NaN\n",
"3 4 porto, v.n.gaia, portugal 17.0\n",
"4 5 farnborough, hants, united kingdom NaN"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Age distribution\n",
"\n",
"The most active users are among 20-30s."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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j4tOF8l8BX42IM+usMw+Yl73dB1g7ws3vCjw5wnXHK4+5PbTbmNttvDD6Mb86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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x141b6761278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"users.Age.hist(bins=[0, 10, 20, 30, 40, 50, 100])\n",
"plt.title('Age Distribution\\n')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Count')\n",
"plt.savefig('system2.png', bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recommendations based on rating counts"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bookRating</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ISBN</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0971880107</th>\n",
" <td>2502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0316666343</th>\n",
" <td>1295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0385504209</th>\n",
" <td>883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0060928336</th>\n",
" <td>732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0312195516</th>\n",
" <td>723</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bookRating\n",
"ISBN \n",
"0971880107 2502\n",
"0316666343 1295\n",
"0385504209 883\n",
"0060928336 732\n",
"0312195516 723"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rating_count = pd.DataFrame(ratings.groupby('ISBN')['bookRating'].count())\n",
"rating_count.sort_values('bookRating', ascending=False).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The book with ISBN \"0971880107\" received the most ratings. Let's find out which books are in the top 5."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>bookTitle</th>\n",
" <th>bookAuthor</th>\n",
" <th>yearOfPublication</th>\n",
" <th>publisher</th>\n",
" <th>imageUrlS</th>\n",
" <th>imageUrlM</th>\n",
" <th>imageUrlL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0971880107</td>\n",
" <td>Wild Animus</td>\n",
" <td>Rich Shapero</td>\n",
" <td>2004</td>\n",
" <td>Too Far</td>\n",
" <td>http://images.amazon.com/images/P/0971880107.0...</td>\n",
" <td>http://images.amazon.com/images/P/0971880107.0...</td>\n",
" <td>http://images.amazon.com/images/P/0971880107.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0316666343</td>\n",
" <td>The Lovely Bones: A Novel</td>\n",
" <td>Alice Sebold</td>\n",
" <td>2002</td>\n",
" <td>Little, Brown</td>\n",
" <td>http://images.amazon.com/images/P/0316666343.0...</td>\n",
" <td>http://images.amazon.com/images/P/0316666343.0...</td>\n",
" <td>http://images.amazon.com/images/P/0316666343.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0385504209</td>\n",
" <td>The Da Vinci Code</td>\n",
" <td>Dan Brown</td>\n",
" <td>2003</td>\n",
" <td>Doubleday</td>\n",
" <td>http://images.amazon.com/images/P/0385504209.0...</td>\n",
" <td>http://images.amazon.com/images/P/0385504209.0...</td>\n",
" <td>http://images.amazon.com/images/P/0385504209.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0060928336</td>\n",
" <td>Divine Secrets of the Ya-Ya Sisterhood: A Novel</td>\n",
" <td>Rebecca Wells</td>\n",
" <td>1997</td>\n",
" <td>Perennial</td>\n",
" <td>http://images.amazon.com/images/P/0060928336.0...</td>\n",
" <td>http://images.amazon.com/images/P/0060928336.0...</td>\n",
" <td>http://images.amazon.com/images/P/0060928336.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0312195516</td>\n",
" <td>The Red Tent (Bestselling Backlist)</td>\n",
" <td>Anita Diamant</td>\n",
" <td>1998</td>\n",
" <td>Picador USA</td>\n",
" <td>http://images.amazon.com/images/P/0312195516.0...</td>\n",
" <td>http://images.amazon.com/images/P/0312195516.0...</td>\n",
" <td>http://images.amazon.com/images/P/0312195516.0...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ISBN bookTitle bookAuthor \\\n",
"0 0971880107 Wild Animus Rich Shapero \n",
"1 0316666343 The Lovely Bones: A Novel Alice Sebold \n",
"2 0385504209 The Da Vinci Code Dan Brown \n",
"3 0060928336 Divine Secrets of the Ya-Ya Sisterhood: A Novel Rebecca Wells \n",
"4 0312195516 The Red Tent (Bestselling Backlist) Anita Diamant \n",
"\n",
" yearOfPublication publisher \\\n",
"0 2004 Too Far \n",
"1 2002 Little, Brown \n",
"2 2003 Doubleday \n",
"3 1997 Perennial \n",
"4 1998 Picador USA \n",
"\n",
" imageUrlS \\\n",
"0 http://images.amazon.com/images/P/0971880107.0... \n",
"1 http://images.amazon.com/images/P/0316666343.0... \n",
"2 http://images.amazon.com/images/P/0385504209.0... \n",
"3 http://images.amazon.com/images/P/0060928336.0... \n",
"4 http://images.amazon.com/images/P/0312195516.0... \n",
"\n",
" imageUrlM \\\n",
"0 http://images.amazon.com/images/P/0971880107.0... \n",
"1 http://images.amazon.com/images/P/0316666343.0... \n",
"2 http://images.amazon.com/images/P/0385504209.0... \n",
"3 http://images.amazon.com/images/P/0060928336.0... \n",
"4 http://images.amazon.com/images/P/0312195516.0... \n",
"\n",
" imageUrlL \n",
"0 http://images.amazon.com/images/P/0971880107.0... \n",
"1 http://images.amazon.com/images/P/0316666343.0... \n",
"2 http://images.amazon.com/images/P/0385504209.0... \n",
"3 http://images.amazon.com/images/P/0060928336.0... \n",
"4 http://images.amazon.com/images/P/0312195516.0... "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"most_rated_books = pd.DataFrame(['0971880107', '0316666343', '0385504209', '0060928336', '0312195516'], index=np.arange(5), columns = ['ISBN'])\n",
"most_rated_books_summary = pd.merge(most_rated_books, books, on='ISBN')\n",
"most_rated_books_summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The book that received the most ratings in this data set is Rich Shapero's Wild Animus. Something in common among these five most rated books - they are fictions or novels. The recommender suggests that novels and fictions are popular and likely receive more ratings. And if someone likes \"Wild Animus\", probably we should recommend him(her) \"The Lovely Bones: A Novel\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recommendations based on correlations\n",
"\n",
"Find out the average rating and the number of ratings each book received."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>ISBN</th>\n",
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" <tr>\n",
" <th>0971880107</th>\n",
" <td>1.019584</td>\n",
" <td>2502</td>\n",
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" <tr>\n",
" <th>0316666343</th>\n",
" <td>4.468726</td>\n",
" <td>1295</td>\n",
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" <tr>\n",
" <th>0385504209</th>\n",
" <td>4.652322</td>\n",
" <td>883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0060928336</th>\n",
" <td>3.448087</td>\n",
" <td>732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0312195516</th>\n",
" <td>4.334716</td>\n",
" <td>723</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bookRating ratingCount\n",
"ISBN \n",
"0971880107 1.019584 2502\n",
"0316666343 4.468726 1295\n",
"0385504209 4.652322 883\n",
"0060928336 3.448087 732\n",
"0312195516 4.334716 723"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"average_rating = pd.DataFrame(ratings.groupby('ISBN')['bookRating'].mean())\n",
"average_rating['ratingCount'] = pd.DataFrame(ratings.groupby('ISBN')['bookRating'].count())\n",
"average_rating.sort_values('ratingCount', ascending=False).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observation: \n",
"\n",
"In this dataet, the book that received the most ratings is not highly rated at all. So if we were set to use recommendations based on rating counts, we would definitely make mistaks here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To ensure statistical significance, users with less than 200 ratings, and books with less than 100 ratings are excluded."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"counts1 = ratings['userID'].value_counts()\n",
"ratings = ratings[ratings['userID'].isin(counts1[counts1 >= 200].index)]\n",
"counts = ratings['bookRating'].value_counts()\n",
"ratings = ratings[ratings['bookRating'].isin(counts[counts >= 100].index)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rating matrix\n",
"\n",
"Convert the table to a 2D matrix. The matrix will be sparse because not every user rate every book."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(905, 207699)\n"
]
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