codealong-clean.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": "markdown",
   "metadata": {},
   "source": [
    "Uncomment and run the cell below if: \n",
    "- you're running this book from the provided binder\n",
    "- you want to pip install the dependent libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install numpy\n",
    "# !pip install pandas\n",
    "# !pip install surprise\n",
    "# !pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'surprise'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-5739f2cd0fe9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVD\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'surprise'"
     ]
    }
   ],
   "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": 4,
   "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": 4,
     "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": 5,
   "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": 5,
     "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": 6,
   "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": 7,
   "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": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['userId'].value_counts()[:10]"
   ]
  },
  {
   "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": [
    "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": 10,
   "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": 11,
   "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": 12,
   "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": 12,
     "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x1195aa9b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate SVD and fit the trainset\n",
    "svd = SVD()\n",
    "svd.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.8946\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8945805030332873"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Prediction(uid=93, iid=1304, r_ui=4.5, est=3.8837808659927755, details={'was_impossible': False}),\n",
       " Prediction(uid=493, iid=3548, r_ui=4.0, est=3.6486496811939904, details={'was_impossible': False}),\n",
       " Prediction(uid=306, iid=132, r_ui=3.0, est=3.074189771476166, details={'was_impossible': False}),\n",
       " Prediction(uid=466, iid=1231, r_ui=4.0, est=3.900451764020538, details={'was_impossible': False}),\n",
       " Prediction(uid=557, iid=239, r_ui=3.0, est=2.747397623445387, details={'was_impossible': False}),\n",
       " Prediction(uid=33, iid=2787, r_ui=4.0, est=3.0822768156617713, details={'was_impossible': False}),\n",
       " Prediction(uid=487, iid=216, r_ui=3.0, est=3.7019738844053407, details={'was_impossible': False}),\n",
       " Prediction(uid=73, iid=8371, r_ui=3.0, est=3.2746996844074356, details={'was_impossible': False}),\n",
       " Prediction(uid=516, iid=316, r_ui=3.0, est=3.4300712865701612, details={'was_impossible': False}),\n",
       " Prediction(uid=587, iid=3551, r_ui=4.5, est=4.14364348941483, details={'was_impossible': False})]"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid=5, iid=100, r_ui=None, est=3.7059739540272427, details={'was_impossible': False})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = 5\n",
    "item = 100\n",
    "svd.predict(user, item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "base_numbering": 1,
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