diff --git a/test.ipynb b/test.ipynb
index a919fa7..aa09426 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 1,
    "source": [
     "# This Notebook is created with VS Code on Windows\r\n",
     "# Create python virtual environment\r\n",
@@ -28,16 +28,17 @@
      "name": "stdout",
      "text": [
       "Requirement already satisfied: pip in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (21.2.4)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (57.4.0)\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: 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: 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: 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",
@@ -47,158 +48,50 @@
       "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: 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: 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: 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: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from plotly) (1.15.0)\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: jupyter-core in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (4.7.1)\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: traitlets>=4.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (5.0.5)\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: 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: setuptools in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (57.4.0)\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",
+      "  Using cached 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",
+      "  Using cached 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",
+      "  Building wheel for scikit-surprise (setup.py): finished with status 'done'\n",
+      "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.1-cp38-cp38-win_amd64.whl size=734505 sha256=072c5d6f9bb826bb28bec68bdb20bf4de7eec14a769df95110fef9cd4def197d\n",
+      "  Stored in directory: c:\\users\\oli\\appdata\\local\\pip\\cache\\wheels\\20\\91\\57\\2965d4cff1b8ac7ed1b6fa25741882af3974b54a31759e10b6\n",
+      "Successfully built 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"
+      "Successfully installed scikit-surprise-1.1.1 surprise-0.1\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"
+      "  WARNING: The script surprise.exe is installed in 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Scripts' which is not on PATH.\n",
+      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
      ]
     }
    ],
@@ -206,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "source": [
     "import numpy as np # maths\r\n",
     "import pandas as pd # data processing\r\n",
@@ -256,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "source": [
     "# Import Tables\r\n",
     "reviews = pd.read_csv('./ml-1m/ratings.dat', names=['userId', 'movieId', 'rating', 'timestamp'], delimiter='::', engine='python')\r\n",
@@ -283,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "source": [
     "# Drop unused Attributes\r\n",
     "reviews.drop(['timestamp'], axis=1, inplace=True) # Time\r\n",
@@ -297,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "source": [
     "# Print movie table\r\n",
     "movies.head()"
@@ -388,17 +281,16 @@
       ]
      },
      "metadata": {},
-     "execution_count": 6
+     "execution_count": 5
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "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",
@@ -440,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "source": [
     "# Plot age kategories\r\n",
     "\r\n",
@@ -460,7 +352,7 @@
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"
      },
      "metadata": {}
@@ -470,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "source": [
     "# Plot gender of users\r\n",
     "gender_counts = users['gender'].value_counts()\r\n",
@@ -489,9 +381,9 @@
      "output_type": "display_data",
      "data": {
       "text/html": [
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        "                            \n",
-       "var gd = document.getElementById('ca10d84b-874e-42f0-a5ff-61bdd61936cc');\n",
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@@ -1378,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "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",
@@ -1508,14 +1400,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 9
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "source": [
     "final_df[final_df['age'] == '18 - 24']['title'].value_counts()[:10].to_frame()"
    ],
@@ -1534,7 +1426,7 @@
        "Saving Private Ryan (1998)                            543\n",
        "Jurassic Park (1993)                                  541\n",
        "Terminator 2: Judgment Day (1991)                     529\n",
-       "Sixth Sense, The (1999)                               514"
+       "Men in Black (1997)                                   514"
       ],
       "text/html": [
        "<div>\n",
@@ -1596,7 +1488,7 @@
        "      <td>529</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Sixth Sense, The (1999)</th>\n",
+       "      <th>Men in Black (1997)</th>\n",
        "      <td>514</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1605,14 +1497,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 39
+     "execution_count": 10
     }
    ],
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 11,
    "source": [
     "# Print movie / user sum\r\n",
     "n_movies = final_df['movieId'].nunique()\r\n",
@@ -1635,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 12,
    "source": [
     "# implement SVD with Python SurPRISE, a Python Recommendation Framework\r\n",
     "\r\n",
@@ -1656,14 +1548,324 @@
    ],
    "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'"
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x28803c98790>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "# Show titels instead of ids\r\n",
+    "id_2_names = dict()\r\n",
+    "for idx, names in zip(movies['movieId'], movies['title']):\r\n",
+    "    id_2_names[idx] = names"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "# function for test set\r\n",
+    "def Build_Anti_Testset4User(user_id):\r\n",
+    "    \r\n",
+    "    fill = trainset.global_mean\r\n",
+    "    anti_testset = list()\r\n",
+    "    u = trainset.to_inner_uid(user_id)\r\n",
+    "    \r\n",
+    "    # ur == users ratings\r\n",
+    "    user_items = set([item_inner_id for (item_inner_id, rating) in trainset.ur[u]])\r\n",
+    "    \r\n",
+    "    anti_testset += [(trainset.to_raw_uid(u), trainset.to_raw_iid(i), fill) for\r\n",
+    "                            i in trainset.all_items() if i not in user_items]\r\n",
+    "    \r\n",
+    "    return anti_testset"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "# Implement Top-X Chart recommender\r\n",
+    "def TopXRec_SVD(user_id, num_recommender=10, latest=False):\r\n",
+    "    \r\n",
+    "    testSet = Build_Anti_Testset4User(user_id)\r\n",
+    "    predict = svd.test(testSet)  # here you can change to SVD++\r\n",
+    "    \r\n",
+    "    recommendation = list()\r\n",
+    "    \r\n",
+    "    for userID, movieID, actualRating, estimatedRating, _ in predict:\r\n",
+    "        intMovieID = int(movieID)\r\n",
+    "        recommendation.append((intMovieID, estimatedRating))\r\n",
+    "        \r\n",
+    "    recommendation.sort(key=lambda x: x[1], reverse=True)\r\n",
+    "    \r\n",
+    "    movie_names = []\r\n",
+    "    movie_ratings = []\r\n",
+    "    \r\n",
+    "    for name, ratings in recommendation[:20]:\r\n",
+    "        movie_names.append(id_2_names[name])\r\n",
+    "        movie_ratings.append(ratings)\r\n",
+    "        \r\n",
+    "    movie_dataframe =  pd.DataFrame({'title': movie_names,\r\n",
+    "                                     'rating': movie_ratings}).merge(movies[['title', 'release_year']],\r\n",
+    "                                            on='title', how='left')\r\n",
+    "    \r\n",
+    "    if latest == True:\r\n",
+    "        return movie_dataframe.sort_values('release_year', ascending=False)[['title', 'rating']].head(num_recommender)\r\n",
+    "    \r\n",
+    "    else:\r\n",
+    "        return movie_dataframe.drop('release_year', axis=1).head(num_recommender)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "source": [
+    "TopXRec_SVD(363, num_recommender=10)\r\n",
+    "TopXRec_SVD(363, num_recommender=10, latest=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                                                title    rating\n",
+       "11  Wallace & Gromit: The Best of Aardman Animatio...  4.705187\n",
+       "2                               Close Shave, A (1995)  4.832506\n",
+       "12                   Shawshank Redemption, The (1994)  4.700431\n",
+       "1                          Wrong Trousers, The (1993)  4.869839\n",
+       "19                            Grand Day Out, A (1992)  4.672615\n",
+       "4                                 Blade Runner (1982)  4.819672\n",
+       "15                              Apocalypse Now (1979)  4.683492\n",
+       "7              One Flew Over the Cuckoo's Nest (1975)  4.738641\n",
+       "16                          Young Frankenstein (1974)  4.682844\n",
+       "10             Monty Python and the Holy Grail (1974)  4.705298"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "        vertical-align: top;\n",
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+       "</style>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
+       "      <td>4.705187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Close Shave, A (1995)</td>\n",
+       "      <td>4.832506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Shawshank Redemption, The (1994)</td>\n",
+       "      <td>4.700431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Wrong Trousers, The (1993)</td>\n",
+       "      <td>4.869839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Grand Day Out, A (1992)</td>\n",
+       "      <td>4.672615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Blade Runner (1982)</td>\n",
+       "      <td>4.819672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Apocalypse Now (1979)</td>\n",
+       "      <td>4.683492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
+       "      <td>4.738641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Young Frankenstein (1974)</td>\n",
+       "      <td>4.682844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Monty Python and the Holy Grail (1974)</td>\n",
+       "      <td>4.705298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "source": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                                                title    rating\n",
+       "11  Wallace & Gromit: The Best of Aardman Animatio...  4.705187\n",
+       "2                               Close Shave, A (1995)  4.832506\n",
+       "12                   Shawshank Redemption, The (1994)  4.700431\n",
+       "1                          Wrong Trousers, The (1993)  4.869839\n",
+       "19                            Grand Day Out, A (1992)  4.672615\n",
+       "4                                 Blade Runner (1982)  4.819672\n",
+       "15                              Apocalypse Now (1979)  4.683492\n",
+       "7              One Flew Over the Cuckoo's Nest (1975)  4.738641\n",
+       "16                          Young Frankenstein (1974)  4.682844\n",
+       "10             Monty Python and the Holy Grail (1974)  4.705298"
+      ],
+      "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",
+       "        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>title</th>\n",
+       "      <th>rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
+       "      <td>4.705187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Close Shave, A (1995)</td>\n",
+       "      <td>4.832506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Shawshank Redemption, The (1994)</td>\n",
+       "      <td>4.700431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Wrong Trousers, The (1993)</td>\n",
+       "      <td>4.869839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Grand Day Out, A (1992)</td>\n",
+       "      <td>4.672615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Blade Runner (1982)</td>\n",
+       "      <td>4.819672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Apocalypse Now (1979)</td>\n",
+       "      <td>4.683492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
+       "      <td>4.738641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Young Frankenstein (1974)</td>\n",
+       "      <td>4.682844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Monty Python and the Holy Grail (1974)</td>\n",
+       "      <td>4.705298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "source": [
+    "# Evaluation\r\n",
+    "\r\n",
+    "# Than predict ratings for all pairs not in training set.\r\n",
+    "testset = trainset.build_anti_testset()\r\n",
+    "predictions_svd = svd.test(testset)\r\n",
+    "print('SVD - RMSE:', accuracy.rmse(predictions_svd, verbose=False))\r\n",
+    "print('SVD - MAE:', accuracy.mae(predictions_svd, verbose=False))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "SVD - RMSE: 0.7015239638211899\n",
+      "SVD - MAE: 0.5429390320069348\n"
      ]
     }
    ],