{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "# This Notebook is created with VS Code on Windows\r\n",
    "# Create python virtual environment\r\n",
    "!python -m venv .venv\r\n",
    "# If you want to use it on macOS/Linux\r\n",
    "# You may need to run sudo apt-get install python3-venv first\r\n",
    "#python3 -m venv .venv\r\n",
    "\r\n",
    "# Install Python Packages\r\n",
    "!pip install --user --upgrade pip\r\n",
    "!pip install --upgrade setuptools\r\n",
    "!pip install --user seaborn\r\n",
    "!pip install --user numpy\r\n",
    "!pip install --user pandas\r\n",
    "!pip install --user matplotlib\r\n",
    "!pip install --user plotly\r\n",
    "!pip install --user nbformat\r\n",
    "!pip install --user surprise\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: pip in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (21.2.4)\n",
      "Requirement already satisfied: seaborn in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (0.11.2)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (3.3.4)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.19.5)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.6.1)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.2.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.19.5)\n",
      "Requirement already satisfied: pandas in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.2.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (2021.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (1.19.5)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (3.3.4)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.19.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (8.1.0)\n",
      "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "Requirement already satisfied: plotly in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (5.3.0)\n",
      "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from plotly) (1.15.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from plotly) (8.0.1)\n",
      "Requirement already satisfied: nbformat in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (5.1.3)\n",
      "Requirement already satisfied: traitlets>=4.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (5.0.5)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (3.2.0)\n",
      "Requirement already satisfied: ipython-genutils in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (0.2.0)\n",
      "Requirement already satisfied: jupyter-core in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (4.7.1)\n",
      "Requirement already satisfied: six>=1.11.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (1.15.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (49.2.1)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (0.18.0)\n",
      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (21.2.0)\n",
      "Requirement already satisfied: pywin32>=1.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jupyter-core->nbformat) (300)\n",
      "Collecting surprise\n",
      "  Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n",
      "Collecting scikit-surprise\n",
      "  Downloading scikit-surprise-1.1.1.tar.gz (11.8 MB)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.0.1)\n",
      "Requirement already satisfied: numpy>=1.11.2 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.19.5)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.6.1)\n",
      "Requirement already satisfied: six>=1.10.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from scikit-surprise->surprise) (1.15.0)\n",
      "Building wheels for collected packages: scikit-surprise\n",
      "  Building wheel for scikit-surprise (setup.py): started\n",
      "  Building wheel for scikit-surprise (setup.py): finished with status 'error'\n",
      "  Running setup.py clean for scikit-surprise\n",
      "Failed to build scikit-surprise\n",
      "Installing collected packages: scikit-surprise, surprise\n",
      "    Running setup.py install for scikit-surprise: started\n",
      "    Running setup.py install for scikit-surprise: finished with status 'error'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "  ERROR: Command errored out with exit status 1:\n",
      "   command: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-wheel-dz4_1ymq'\n",
      "       cwd: C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-install-jibnz200\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\n",
      "  Complete output (49 lines):\n",
      "  running bdist_wheel\n",
      "  running build\n",
      "  running build_py\n",
      "  creating build\n",
      "  creating build\\lib.win-amd64-3.8\n",
      "  creating build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\accuracy.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\builtin_datasets.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\dataset.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\dump.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\reader.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\trainset.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\utils.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\__main__.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "  creating build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "  copying surprise\\model_selection\\search.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "  copying surprise\\model_selection\\split.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "  copying surprise\\model_selection\\validation.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "  copying surprise\\model_selection\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "  creating build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\algo_base.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\baseline_only.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\knns.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\predictions.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\random_pred.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  running egg_info\n",
      "  writing scikit_surprise.egg-info\\PKG-INFO\n",
      "  writing dependency_links to scikit_surprise.egg-info\\dependency_links.txt\n",
      "  writing entry points to scikit_surprise.egg-info\\entry_points.txt\n",
      "  writing requirements to scikit_surprise.egg-info\\requires.txt\n",
      "  writing top-level names to scikit_surprise.egg-info\\top_level.txt\n",
      "  reading manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n",
      "  reading manifest template 'MANIFEST.in'\n",
      "  writing manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n",
      "  copying surprise\\similarities.c -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\similarities.pyx -> build\\lib.win-amd64-3.8\\surprise\n",
      "  copying surprise\\prediction_algorithms\\co_clustering.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\matrix_factorization.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\optimize_baselines.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\slope_one.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\co_clustering.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\matrix_factorization.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\optimize_baselines.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  copying surprise\\prediction_algorithms\\slope_one.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "  running build_ext\n",
      "  building 'surprise.similarities' extension\n",
      "  error: Microsoft Visual C++ 14.0 is required. Get it with \"Build Tools for Visual Studio\": https://visualstudio.microsoft.com/downloads/\n",
      "  ----------------------------------------\n",
      "  ERROR: Failed building wheel for scikit-surprise\n",
      "    ERROR: Command errored out with exit status 1:\n",
      "     command: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-record-ivcio2vz\\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Include\\scikit-surprise'\n",
      "         cwd: C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-install-jibnz200\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\n",
      "    Complete output (49 lines):\n",
      "    running install\n",
      "    running build\n",
      "    running build_py\n",
      "    creating build\n",
      "    creating build\\lib.win-amd64-3.8\n",
      "    creating build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\accuracy.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\builtin_datasets.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\dataset.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\dump.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\reader.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\trainset.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\utils.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\__main__.py -> build\\lib.win-amd64-3.8\\surprise\n",
      "    creating build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "    copying surprise\\model_selection\\search.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "    copying surprise\\model_selection\\split.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "    copying surprise\\model_selection\\validation.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "    copying surprise\\model_selection\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n",
      "    creating build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\algo_base.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\baseline_only.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\knns.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\predictions.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\random_pred.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    running egg_info\n",
      "    writing scikit_surprise.egg-info\\PKG-INFO\n",
      "    writing dependency_links to scikit_surprise.egg-info\\dependency_links.txt\n",
      "    writing entry points to scikit_surprise.egg-info\\entry_points.txt\n",
      "    writing requirements to scikit_surprise.egg-info\\requires.txt\n",
      "    writing top-level names to scikit_surprise.egg-info\\top_level.txt\n",
      "    reading manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n",
      "    reading manifest template 'MANIFEST.in'\n",
      "    writing manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n",
      "    copying surprise\\similarities.c -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\similarities.pyx -> build\\lib.win-amd64-3.8\\surprise\n",
      "    copying surprise\\prediction_algorithms\\co_clustering.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\matrix_factorization.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\optimize_baselines.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\slope_one.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\co_clustering.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\matrix_factorization.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\optimize_baselines.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    copying surprise\\prediction_algorithms\\slope_one.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n",
      "    running build_ext\n",
      "    building 'surprise.similarities' extension\n",
      "    error: Microsoft Visual C++ 14.0 is required. Get it with \"Build Tools for Visual Studio\": https://visualstudio.microsoft.com/downloads/\n",
      "    ----------------------------------------\n",
      "ERROR: Command errored out with exit status 1: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-record-ivcio2vz\\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Include\\scikit-surprise' Check the logs for full command output.\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import numpy as np # maths\r\n",
    "import pandas as pd # data processing\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import seaborn as sns\r\n",
    "import os\r\n",
    "import re\r\n",
    "\r\n",
    "from plotly.offline import init_notebook_mode, iplot\r\n",
    "import plotly.graph_objs as go\r\n",
    "import plotly.offline as py\r\n",
    "py.init_notebook_mode(connected=True)\r\n",
    "\r\n",
    "import warnings\r\n",
    "warnings.filterwarnings('ignore')\r\n",
    "\r\n",
    "plt.style.use('fivethirtyeight')\r\n",
    "plt.rcParams['figure.figsize'] = [18, 8]"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.4.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Import Tables\r\n",
    "reviews = pd.read_csv('./ml-1m/ratings.dat', names=['userId', 'movieId', 'rating', 'timestamp'], delimiter='::', engine='python')\r\n",
    "movies = pd.read_csv('./ml-1m/movies.dat', names=['movieId', 'title', 'genres'], delimiter='::', engine='python')\r\n",
    "users = pd.read_csv('./ml-1m/users.dat', names=['userId', 'gender', 'age', 'occupation', 'zip'], delimiter='::', engine='python')\r\n",
    "\r\n",
    "# Print Table shape\r\n",
    "print('Reviews shape:', reviews.shape)\r\n",
    "print('Users shape:', users.shape)\r\n",
    "print('Movies shape:', movies.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Reviews shape: (1000209, 4)\n",
      "Users shape: (6040, 5)\n",
      "Movies shape: (3883, 3)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Drop unused Attributes\r\n",
    "reviews.drop(['timestamp'], axis=1, inplace=True) # Time\r\n",
    "users.drop(['zip'], axis=1, inplace=True) # Zip-Code\r\n",
    "\r\n",
    "# Extract the movie year from title to extra attrbute\r\n",
    "movies['release_year'] = movies['title'].str.extract(r'(?:\\((\\d{4})\\))?\\s*$', expand=False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Print movie table\r\n",
    "movies.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   movieId                               title                        genres  \\\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy   \n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy   \n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance   \n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama   \n",
       "4        5  Father of the Bride Part II (1995)                        Comedy   \n",
       "\n",
       "  release_year  \n",
       "0         1995  \n",
       "1         1995  \n",
       "2         1995  \n",
       "3         1995  \n",
       "4         1995  "
      ],
      "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Changed feature values based on README_users.txt\r\n",
    "\r\n",
    "ages_map = {1: 'Under 18',\r\n",
    "            18: '18 - 24',\r\n",
    "            25: '25 - 34',\r\n",
    "            35: '35 - 44',\r\n",
    "            45: '45 - 49',\r\n",
    "            50: '50 - 55',\r\n",
    "            56: '56+'}\r\n",
    "\r\n",
    "occupations_map = {0: 'Not specified',\r\n",
    "                   1: 'Academic / Educator',\r\n",
    "                   2: 'Artist',\r\n",
    "                   3: 'Clerical / Admin',\r\n",
    "                   4: 'College / Grad Student',\r\n",
    "                   5: 'Customer Service',\r\n",
    "                   6: 'Doctor / Health Care',\r\n",
    "                   7: 'Executive / Managerial',\r\n",
    "                   8: 'Farmer',\r\n",
    "                   9: 'Homemaker',\r\n",
    "                   10: 'K-12 student',\r\n",
    "                   11: 'Lawyer',\r\n",
    "                   12: 'Programmer',\r\n",
    "                   13: 'Retired',\r\n",
    "                   14: 'Sales / Marketing',\r\n",
    "                   15: 'Scientist',\r\n",
    "                   16: 'Self-Employed',\r\n",
    "                   17: 'Technician / Engineer',\r\n",
    "                   18: 'Tradesman / Craftsman',\r\n",
    "                   19: 'Unemployed',\r\n",
    "                   20: 'Writer'}\r\n",
    "\r\n",
    "gender_map = {'M': 'Male', 'F': 'Female'}\r\n",
    "\r\n",
    "users['age'] = users['age'].map(ages_map)\r\n",
    "users['occupation'] = users['occupation'].map(occupations_map)\r\n",
    "users['gender'] = users['gender'].map(gender_map)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Plot age kategories\r\n",
    "\r\n",
    "age_reindex = ['Under 18', '18 - 24', '25 - 34', '35 - 44', '45 - 49', '50 - 55', '56+']\r\n",
    "age_counts = users['age'].value_counts().reindex(age_reindex)\r\n",
    "sns.barplot(x=age_counts.values,\r\n",
    "            y=age_counts.index,\r\n",
    "            palette='magma').set_title(\r\n",
    "                'Users age', fontsize=12)\r\n",
    "\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Plot gender of users\r\n",
    "gender_counts = users['gender'].value_counts()\r\n",
    "colors1 = ['lightblue', 'pink']\r\n",
    "pie = go.Pie(labels=gender_counts.index,\r\n",
    "             values=gender_counts.values,\r\n",
    "             marker=dict(colors=colors1),\r\n",
    "             hole=0.5)\r\n",
    "layout = go.Layout(title='Gender Users', font=dict(size=12), legend=dict(orientation='h'))\r\n",
    "\r\n",
    "fig = go.Figure(data=[pie], layout=layout)\r\n",
    "py.iplot(fig)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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   "source": [
    "# Merge reviews, movie and user dataset\r\n",
    "final_df = reviews.merge(movies, on='movieId', how='left').merge(users, on='userId', how='left')\r\n",
    "print('final_df shape:', final_df.shape)\r\n",
    "final_df.head()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "final_df shape: (1000209, 9)\n"
     ]
    },
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       "   userId  movieId  rating                                   title  \\\n",
       "0       1     1193       5  One Flew Over the Cuckoo's Nest (1975)   \n",
       "1       1      661       3        James and the Giant Peach (1996)   \n",
       "2       1      914       3                     My Fair Lady (1964)   \n",
       "3       1     3408       4                  Erin Brockovich (2000)   \n",
       "4       1     2355       5                    Bug's Life, A (1998)   \n",
       "\n",
       "                         genres release_year  gender       age    occupation  \n",
       "0                         Drama         1975  Female  Under 18  K-12 student  \n",
       "1  Animation|Children's|Musical         1996  Female  Under 18  K-12 student  \n",
       "2               Musical|Romance         1964  Female  Under 18  K-12 student  \n",
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   "source": [
    "final_df[final_df['age'] == '18 - 24']['title'].value_counts()[:10].to_frame()"
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       "                                                    title\n",
       "American Beauty (1999)                                715\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)     586\n",
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       "Matrix, The (1999)                                    567\n",
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  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "# Print movie / user sum\r\n",
    "n_movies = final_df['movieId'].nunique()\r\n",
    "n_users = final_df['userId'].nunique()\r\n",
    "\r\n",
    "print('Number of movies:', n_movies)\r\n",
    "print('Number of users:', n_users)                                "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of movies: 3706\n",
      "Number of users: 6040\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "# implement SVD with Python SurPRISE, a Python Recommendation Framework\r\n",
    "\r\n",
    "from surprise import Reader, Dataset, SVD, SVDpp\r\n",
    "from surprise import accuracy\r\n",
    "\r\n",
    "reader = Reader(rating_scale=(1, 5))\r\n",
    "dataset = Dataset.load_from_df(final_df[['userId', 'movieId', 'rating']], reader=reader)\r\n",
    "\r\n",
    "svd = SVD(n_factors=50)\r\n",
    "svd_plusplus = SVDpp(n_factors=50)\r\n",
    "\r\n",
    "# train with SVD\r\n",
    "trainset = dataset.build_full_trainset()\r\n",
    "svd.fit(trainset)\r\n",
    "# train with SVD++, ATTENTION this take a LONG TIME\r\n",
    "# svd_plusplus.fit(trainset)\r\n"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'surprise'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-ae291f0713b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# implement SVD with Python SurPRISE, a Python Recommendation Framework\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mReader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSVD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSVDpp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'surprise'"
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