codealong.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Science Behind Netflix Recommendations: Code-Along\n",
    "\n",
    "One of the easiest libraries to use for recommendation systems is Surprise, which stands for **Simple Python RecommendatIon System Engine**. In this notebook, we'll code a recommendation system using the Surprise Library's Singular Value Decomposition algorithm!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data Science Process\n",
    "\n",
    "![osemn](https://miro.medium.com/max/3870/1*eE8DP4biqtaIK3aIy1S2zA.png \"OSEMN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install numpy\n",
    "# !pip install pandas\n",
    "# !pip install surprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from surprise import Dataset, Reader\n",
    "from surprise import SVD\n",
    "from surprise import accuracy\n",
    "from surprise.model_selection import cross_validate, train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Reading in the data and a simple exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First: where is this data from?\n",
    "\n",
    "https://www.kaggle.com/rounakbanik/the-movies-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100004, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1263</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1287</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1293</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1339</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1260759125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205\n",
       "5       1     1263     2.0  1260759151\n",
       "6       1     1287     2.0  1260759187\n",
       "7       1     1293     2.0  1260759148\n",
       "8       1     1339     3.5  1260759125\n",
       "9       1     1343     2.0  1260759131"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ratings.csv') # pandas' read_csv function\n",
    "print(df.shape) # how many rows, columns are in the dataframe\n",
    "df.head(10) # previewing the first 10 rows "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a) Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    28750\n",
       "3.0    20064\n",
       "5.0    15095\n",
       "3.5    10538\n",
       "4.5     7723\n",
       "2.0     7271\n",
       "2.5     4449\n",
       "1.0     3326\n",
       "1.5     1687\n",
       "0.5     1101\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts() shows us how many times each value appears in a column\n",
    "ratings = df['rating'].value_counts()\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution in matplotlib\n",
    "ratings_sorted = sorted(list(zip(ratings.index, ratings)))\n",
    "plt.bar([r[0] for r in ratings_sorted], [r[1] for r in ratings_sorted], color='maroon')\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"# of Ratings\")\n",
    "plt.title(\"Distribution of Ratings\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users:  671\n",
      "Average Number of Reviews per User:  149.03725782414307\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of users: \", df.userId.nunique()) \n",
    "print(\"Average Number of Reviews per User: \", df.shape[0]/df.userId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "547    2391\n",
       "564    1868\n",
       "624    1735\n",
       "15     1700\n",
       "73     1610\n",
       "452    1340\n",
       "468    1291\n",
       "380    1063\n",
       "311    1019\n",
       "30     1011\n",
       "Name: userId, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['userId'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings_per_user = df['userId'].value_counts()\n",
    "ratings_per_user = sorted(list(zip(ratings_per_user.index, ratings_per_user)))\n",
    "plt.bar([r[0] for r in ratings_per_user], [r[1] for r in ratings_per_user], color='orange')\n",
    "plt.xlabel(\"User IDs\")\n",
    "plt.ylabel(\"# of Reviews\")\n",
    "plt.title(\"Number of Reviews per User\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_ratings = [r[1] for r in ratings_per_user]\n",
    "sorted_user_ratings = sorted(Counter(user_ratings).items())\n",
    "plt.bar([r[0] for r in sorted_user_ratings][:-1], [r[1] for r in sorted_user_ratings][:-1], color='green')\n",
    "plt.xlabel(\"Number of Users\")\n",
    "plt.ylabel(\"# of Reviews\")\n",
    "plt.title(\"Distribution of Review Frequency per User\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c) Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of movies:  9066\n",
      "Average Number of Reviews per Movie:  11.030664019413193\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of movies: \", df.movieId.nunique())\n",
    "print(\"Average Number of Reviews per Movie: \", df.shape[0]/df.movieId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356     341\n",
       "296     324\n",
       "318     311\n",
       "593     304\n",
       "260     291\n",
       "480     274\n",
       "2571    259\n",
       "1       247\n",
       "527     244\n",
       "589     237\n",
       "Name: movieId, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['movieId'].value_counts().iloc[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Implementing Surprise's SVD\n",
    "To read more about SVD and its hyperparameters:\n",
    "https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for Surprise, we only need three columns from the dataset\n",
    "data = df[['userId', 'movieId', 'rating']]\n",
    "reader = Reader(line_format='user item rating', sep=',')\n",
    "data = Dataset.load_from_df(data, reader=reader)\n",
    "\n",
    "# train-test-split\n",
    "trainset, testset = train_test_split(data, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x11badea20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate SVD and fit the trainset\n",
    "svd = SVD()\n",
    "svd.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.8984\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8983519884553468"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = svd.test(testset)\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Prediction(uid=664, iid=3969, r_ui=3.5, est=3.404396893741621, details={'was_impossible': False}),\n",
       " Prediction(uid=443, iid=1196, r_ui=5.0, est=5, details={'was_impossible': False}),\n",
       " Prediction(uid=306, iid=57, r_ui=2.0, est=3.4818983449323704, details={'was_impossible': False}),\n",
       " Prediction(uid=86, iid=595, r_ui=3.0, est=3.6601180836627067, details={'was_impossible': False}),\n",
       " Prediction(uid=134, iid=96079, r_ui=5.0, est=4.18934267035494, details={'was_impossible': False}),\n",
       " Prediction(uid=165, iid=7360, r_ui=4.0, est=3.1076593554636966, details={'was_impossible': False}),\n",
       " Prediction(uid=77, iid=2788, r_ui=4.0, est=3.7111127638515065, details={'was_impossible': False}),\n",
       " Prediction(uid=97, iid=588, r_ui=0.5, est=2.6784997499643457, details={'was_impossible': False}),\n",
       " Prediction(uid=463, iid=2000, r_ui=4.0, est=3.540008332638215, details={'was_impossible': False}),\n",
       " Prediction(uid=595, iid=1264, r_ui=5.0, est=4.305528870037745, details={'was_impossible': False})]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a look at the first 10 rows of our test set\n",
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users:  671\n",
      "Number of movies:  9066\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of users: \", df.userId.nunique()) \n",
    "print(\"Number of movies: \", df.movieId.nunique()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid=5, iid=100, r_ui=None, est=3.678383406675281, details={'was_impossible': False})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = 5\n",
    "item = 100\n",
    "svd.predict(user, item)"
   ]
  }
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