assignment3SpecOriginal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Association analysis and Recommender Systems\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This project is split into 2 parts.\n",
    "First we look at the Association analysis for a popular dataset [`Online Retail`](http://archive.ics.uci.edu/ml/machine-learning-databases/00352) followed by Recommender Systems for [MovieLens dataset](http://www.grouplens.org/system/files/ml-1m.zip)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can be viewed at this link as all images may not render completely: https://nbviewer.jupyter.org/github/iamfarazbaig/assignment3/blob/master/assignment3SpecOriginal.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "import matplotlib.pyplot as plt\n",
    "import requests, os\n",
    "\n",
    "xlUrl = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx'\n",
    "xlFile = 'data/Online Retail.xlsx'\n",
    "dataFile = xlFile\n",
    "url = xlUrl\n",
    "if not os.path.exists('data'):\n",
    "    os.makedirs('data')\n",
    "if not os.path.isfile(dataFile):\n",
    "  r = requests.get(url)\n",
    "  with open(dataFile, 'wb') as f:\n",
    "    f.write(r.content)\n",
    "if (dataFile == xlFile):\n",
    "  df = pd.read_excel(dataFile)\n",
    "else:\n",
    "  df = pd.read_csv(dataFile)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Association Analysis does not require rigorous data cleaning, it does make sense to remove extraneous records, such as those with null invoice numbers and canceled orders. It’s also useful to remove extraneous spaces in the product descriptions and convert all the invoice numbers to string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Description'] = df['Description'].str.strip()\n",
    "df.dropna(axis=0, subset=['InvoiceNo'], inplace=True)\n",
    "df['InvoiceNo'] = df['InvoiceNo'].astype('str')\n",
    "df = df[~df['InvoiceNo'].str.contains('C')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Visualisation of no. of Orders for all Countries\n",
    "I'm using a log scale just for visualisation as the UK dominates the market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import ceil\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import plotly.graph_objs as go\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "grouped  = df.groupby(['Country']).count()['Quantity'].sort_values(ascending=False)\n",
    "\n",
    "data_plot = [go.Bar(x=grouped.index, y=grouped.values)]\n",
    "layout = go.Layout(yaxis={'type':'log', 'autorange':True, 'title':'Log10 number of orders'},xaxis={'title':'country'})\n",
    "fig = go.Figure(data=data_plot, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that in the `Quantity` column, the count for Germany is equal to the to the number of records required(9042) by hovering over the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating subsets of dataframe for each unique country, in this case only Germany\n",
    "Code source: https://www.kaggle.com/ostrowski/market-basket-analysis-exploring-e-commerce-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def df_per_country(df):\n",
    "    import re\n",
    "    df_dict = {}\n",
    "    unique_countries, counts = np.unique(df.Country, return_counts=True)\n",
    "    for country in unique_countries:\n",
    "        df_dict[\"df_{}\".format(re.sub('[\\s+]', '', country))] = df[df.Country == country].copy()\n",
    "        df_dict[\"df_{}\".format(re.sub('[\\s+]', '', country))].drop('Country', axis=1, inplace=True)\n",
    "    return df_dict\n",
    "\n",
    "# Trick to convert dictionary key/values into variables\n",
    "# This way we don't need to access dfs by df_dict['df_France'] for example\n",
    "df_dict = df_per_country(df)\n",
    "locals().update(df_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series plot function summarizing df_Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def series_plot(df, by1, by2, by3, period='D'):\n",
    "    df_ts = df.reset_index().pivot_table(index='InvoiceDate', \n",
    "                                values=['InvoiceNo', 'Quantity', 'UnitPrice'], \n",
    "                                aggfunc=('count', 'sum'))\n",
    "    df_ts = df_ts.loc[:, [('InvoiceNo', 'count'), ('Quantity', 'sum'), ('UnitPrice', 'sum')]]\n",
    "    df_ts.columns = df_ts.columns.droplevel(1)\n",
    "    plt.figure(figsize=(14, 6))\n",
    "    \n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.plot(df_ts.resample(period).sum().bfill()[[by1]], color='navy')\n",
    "    plt.title('{}'.format(by1));\n",
    "    plt.xticks(rotation=60);\n",
    "    plt.subplot(2, 2, 2)\n",
    "    plt.title('{}'.format(by2));\n",
    "    plt.plot(df_ts.resample(period).sum().bfill()[[by2]], label='Total Sale', color='orange');\n",
    "    plt.xticks(rotation=60)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    plt.figure(figsize=(14, 8))\n",
    "    plt.title('{}'.format(by3));\n",
    "    plt.plot(df_ts.resample(period).sum().bfill()[[by3]], label='Total Invoices', color='green');\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_plot(df, 'Quantity', 'UnitPrice', 'InvoiceNo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I’ll rearrange the data with each product one hot encoded and one transaction per row. \n",
    "One hot encoding is a data transformation technique where categorical values are converted into columns, with the value of 1 entered where a categorical value is present. \n",
    "I will also limit the scope of the dataset to one country, in this case Germany, to compare consumer behavior in an individual market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>Description</th>\n",
       "      <th>10 COLOUR SPACEBOY PEN</th>\n",
       "      <th>12 COLOURED PARTY BALLOONS</th>\n",
       "      <th>12 IVORY ROSE PEG PLACE SETTINGS</th>\n",
       "      <th>12 MESSAGE CARDS WITH ENVELOPES</th>\n",
       "      <th>12 PENCIL SMALL TUBE WOODLAND</th>\n",
       "      <th>12 PENCILS SMALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS SMALL TUBE SKULL</th>\n",
       "      <th>12 PENCILS TALL TUBE POSY</th>\n",
       "      <th>12 PENCILS TALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS TALL TUBE SKULLS</th>\n",
       "      <th>...</th>\n",
       "      <th>YULETIDE IMAGES GIFT WRAP SET</th>\n",
       "      <th>ZINC  HEART T-LIGHT HOLDER</th>\n",
       "      <th>ZINC  STAR T-LIGHT HOLDER</th>\n",
       "      <th>ZINC BOX SIGN HOME</th>\n",
       "      <th>ZINC FOLKART SLEIGH BELLS</th>\n",
       "      <th>ZINC HEART LATTICE T-LIGHT HOLDER</th>\n",
       "      <th>ZINC METAL HEART DECORATION</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STAR LARGE</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n",
       "      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <th>537198</th>\n",
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       "    <tr>\n",
       "      <th>537201</th>\n",
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       "    <tr>\n",
       "      <th>537212</th>\n",
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       "    <tr>\n",
       "      <th>537250</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 1695 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
       "InvoiceNo                                                         \n",
       "536527                          0.0                         0.0   \n",
       "536840                          0.0                         0.0   \n",
       "536861                          0.0                         0.0   \n",
       "536967                          0.0                         0.0   \n",
       "536983                          0.0                         0.0   \n",
       "537197                          0.0                         0.0   \n",
       "537198                          0.0                         0.0   \n",
       "537201                          0.0                         0.0   \n",
       "537212                          0.0                         0.0   \n",
       "537250                          0.0                         0.0   \n",
       "\n",
       "Description  12 IVORY ROSE PEG PLACE SETTINGS  \\\n",
       "InvoiceNo                                       \n",
       "536527                                    0.0   \n",
       "536840                                    0.0   \n",
       "536861                                    0.0   \n",
       "536967                                    0.0   \n",
       "536983                                    0.0   \n",
       "537197                                    0.0   \n",
       "537198                                    0.0   \n",
       "537201                                    0.0   \n",
       "537212                                    0.0   \n",
       "537250                                    0.0   \n",
       "\n",
       "Description  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\n",
       "InvoiceNo                                                                     \n",
       "536527                                   0.0                            0.0   \n",
       "536840                                   0.0                            0.0   \n",
       "536861                                   0.0                            0.0   \n",
       "536967                                   0.0                            0.0   \n",
       "536983                                   0.0                            0.0   \n",
       "537197                                   0.0                            0.0   \n",
       "537198                                   0.0                            0.0   \n",
       "537201                                   0.0                            0.0   \n",
       "537212                                   0.0                            0.0   \n",
       "537250                                   0.0                            0.0   \n",
       "\n",
       "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\n",
       "InvoiceNo                                                                       \n",
       "536527                                       0.0                          0.0   \n",
       "536840                                       0.0                          0.0   \n",
       "536861                                       0.0                          0.0   \n",
       "536967                                       0.0                          0.0   \n",
       "536983                                       0.0                          0.0   \n",
       "537197                                       0.0                          0.0   \n",
       "537198                                       0.0                          0.0   \n",
       "537201                                       0.0                          0.0   \n",
       "537212                                       0.0                          0.0   \n",
       "537250                                       0.0                          0.0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\n",
       "InvoiceNo                                                                    \n",
       "536527                             0.0                                 0.0   \n",
       "536840                             0.0                                 0.0   \n",
       "536861                             0.0                                 0.0   \n",
       "536967                             0.0                                 0.0   \n",
       "536983                             0.0                                 0.0   \n",
       "537197                             0.0                                 0.0   \n",
       "537198                             0.0                                 0.0   \n",
       "537201                             0.0                                 0.0   \n",
       "537212                             0.0                                 0.0   \n",
       "537250                             0.0                                 0.0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE SKULLS                ...                 \\\n",
       "InvoiceNo                                               ...                  \n",
       "536527                               0.0                ...                  \n",
       "536840                               0.0                ...                  \n",
       "536861                               0.0                ...                  \n",
       "536967                               0.0                ...                  \n",
       "536983                               0.0                ...                  \n",
       "537197                               0.0                ...                  \n",
       "537198                               0.0                ...                  \n",
       "537201                               0.0                ...                  \n",
       "537212                               0.0                ...                  \n",
       "537250                               0.0                ...                  \n",
       "\n",
       "Description  YULETIDE IMAGES GIFT WRAP SET  ZINC  HEART T-LIGHT HOLDER  \\\n",
       "InvoiceNo                                                                \n",
       "536527                                 0.0                         0.0   \n",
       "536840                                 0.0                         0.0   \n",
       "536861                                 0.0                         0.0   \n",
       "536967                                 0.0                         0.0   \n",
       "536983                                 0.0                         0.0   \n",
       "537197                                 0.0                         0.0   \n",
       "537198                                 0.0                         0.0   \n",
       "537201                                 0.0                         0.0   \n",
       "537212                                 0.0                         0.0   \n",
       "537250                                 0.0                         0.0   \n",
       "\n",
       "Description  ZINC  STAR T-LIGHT HOLDER  ZINC BOX SIGN HOME  \\\n",
       "InvoiceNo                                                    \n",
       "536527                             0.0                 0.0   \n",
       "536840                             0.0                 0.0   \n",
       "536861                             0.0                 0.0   \n",
       "536967                             0.0                 0.0   \n",
       "536983                             0.0                 0.0   \n",
       "537197                             0.0                 0.0   \n",
       "537198                             0.0                 0.0   \n",
       "537201                             0.0                 0.0   \n",
       "537212                             0.0                 0.0   \n",
       "537250                             0.0                 0.0   \n",
       "\n",
       "Description  ZINC FOLKART SLEIGH BELLS  ZINC HEART LATTICE T-LIGHT HOLDER  \\\n",
       "InvoiceNo                                                                   \n",
       "536527                             0.0                                0.0   \n",
       "536840                             0.0                                0.0   \n",
       "536861                             0.0                                0.0   \n",
       "536967                             0.0                                0.0   \n",
       "536983                             0.0                                0.0   \n",
       "537197                             0.0                                0.0   \n",
       "537198                             0.0                                0.0   \n",
       "537201                             0.0                                0.0   \n",
       "537212                             0.0                                0.0   \n",
       "537250                             0.0                                0.0   \n",
       "\n",
       "Description  ZINC METAL HEART DECORATION  ZINC T-LIGHT HOLDER STAR LARGE  \\\n",
       "InvoiceNo                                                                  \n",
       "536527                               0.0                             0.0   \n",
       "536840                               0.0                             0.0   \n",
       "536861                               0.0                             0.0   \n",
       "536967                               0.0                             0.0   \n",
       "536983                               0.0                             0.0   \n",
       "537197                               0.0                             0.0   \n",
       "537198                               0.0                             0.0   \n",
       "537201                               0.0                             0.0   \n",
       "537212                               0.0                             0.0   \n",
       "537250                               0.0                             0.0   \n",
       "\n",
       "Description  ZINC T-LIGHT HOLDER STARS SMALL  ZINC WILLIE WINKIE  CANDLE STICK  \n",
       "InvoiceNo                                                                       \n",
       "536527                                   0.0                               0.0  \n",
       "536840                                   0.0                               0.0  \n",
       "536861                                   0.0                               0.0  \n",
       "536967                                   0.0                               0.0  \n",
       "536983                                   0.0                               0.0  \n",
       "537197                                   0.0                               0.0  \n",
       "537198                                   0.0                               0.0  \n",
       "537201                                   0.0                               0.0  \n",
       "537212                                   0.0                               0.0  \n",
       "537250                                   0.0                               0.0  \n",
       "\n",
       "[10 rows x 1695 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket = (df[df['Country'] ==\"Germany\"]\n",
    "          .groupby(['InvoiceNo', 'Description'])['Quantity']\n",
    "          .sum().unstack().reset_index().fillna(0)\n",
    "          .set_index('InvoiceNo'))\n",
    "basket.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of zeros in the data but we also need to make sure any positive values are converted to a 1 and anything less the 0 is set to 0. This step will complete the one hot encoding of the data and remove the `Postage` column (since that charge is not one we wish to explore):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Description</th>\n",
       "      <th>10 COLOUR SPACEBOY PEN</th>\n",
       "      <th>12 COLOURED PARTY BALLOONS</th>\n",
       "      <th>12 IVORY ROSE PEG PLACE SETTINGS</th>\n",
       "      <th>12 MESSAGE CARDS WITH ENVELOPES</th>\n",
       "      <th>12 PENCIL SMALL TUBE WOODLAND</th>\n",
       "      <th>12 PENCILS SMALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS SMALL TUBE SKULL</th>\n",
       "      <th>12 PENCILS TALL TUBE POSY</th>\n",
       "      <th>12 PENCILS TALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS TALL TUBE SKULLS</th>\n",
       "      <th>...</th>\n",
       "      <th>YULETIDE IMAGES GIFT WRAP SET</th>\n",
       "      <th>ZINC  HEART T-LIGHT HOLDER</th>\n",
       "      <th>ZINC  STAR T-LIGHT HOLDER</th>\n",
       "      <th>ZINC BOX SIGN HOME</th>\n",
       "      <th>ZINC FOLKART SLEIGH BELLS</th>\n",
       "      <th>ZINC HEART LATTICE T-LIGHT HOLDER</th>\n",
       "      <th>ZINC METAL HEART DECORATION</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STAR LARGE</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n",
       "      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536840</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536861</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536967</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536983</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537197</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537198</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537201</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537212</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537250</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 1694 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
       "InvoiceNo                                                         \n",
       "536527                            0                           0   \n",
       "536840                            0                           0   \n",
       "536861                            0                           0   \n",
       "536967                            0                           0   \n",
       "536983                            0                           0   \n",
       "537197                            0                           0   \n",
       "537198                            0                           0   \n",
       "537201                            0                           0   \n",
       "537212                            0                           0   \n",
       "537250                            0                           0   \n",
       "\n",
       "Description  12 IVORY ROSE PEG PLACE SETTINGS  \\\n",
       "InvoiceNo                                       \n",
       "536527                                      0   \n",
       "536840                                      0   \n",
       "536861                                      0   \n",
       "536967                                      0   \n",
       "536983                                      0   \n",
       "537197                                      0   \n",
       "537198                                      0   \n",
       "537201                                      0   \n",
       "537212                                      0   \n",
       "537250                                      0   \n",
       "\n",
       "Description  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\n",
       "InvoiceNo                                                                     \n",
       "536527                                     0                              0   \n",
       "536840                                     0                              0   \n",
       "536861                                     0                              0   \n",
       "536967                                     0                              0   \n",
       "536983                                     0                              0   \n",
       "537197                                     0                              0   \n",
       "537198                                     0                              0   \n",
       "537201                                     0                              0   \n",
       "537212                                     0                              0   \n",
       "537250                                     0                              0   \n",
       "\n",
       "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\n",
       "InvoiceNo                                                                       \n",
       "536527                                         0                            0   \n",
       "536840                                         0                            0   \n",
       "536861                                         0                            0   \n",
       "536967                                         0                            0   \n",
       "536983                                         0                            0   \n",
       "537197                                         0                            0   \n",
       "537198                                         0                            0   \n",
       "537201                                         0                            0   \n",
       "537212                                         0                            0   \n",
       "537250                                         0                            0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\n",
       "InvoiceNo                                                                    \n",
       "536527                               0                                   0   \n",
       "536840                               0                                   0   \n",
       "536861                               0                                   0   \n",
       "536967                               0                                   0   \n",
       "536983                               0                                   0   \n",
       "537197                               0                                   0   \n",
       "537198                               0                                   0   \n",
       "537201                               0                                   0   \n",
       "537212                               0                                   0   \n",
       "537250                               0                                   0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE SKULLS                ...                 \\\n",
       "InvoiceNo                                               ...                  \n",
       "536527                                 0                ...                  \n",
       "536840                                 0                ...                  \n",
       "536861                                 0                ...                  \n",
       "536967                                 0                ...                  \n",
       "536983                                 0                ...                  \n",
       "537197                                 0                ...                  \n",
       "537198                                 0                ...                  \n",
       "537201                                 0                ...                  \n",
       "537212                                 0                ...                  \n",
       "537250                                 0                ...                  \n",
       "\n",
       "Description  YULETIDE IMAGES GIFT WRAP SET  ZINC  HEART T-LIGHT HOLDER  \\\n",
       "InvoiceNo                                                                \n",
       "536527                                   0                           0   \n",
       "536840                                   0                           0   \n",
       "536861                                   0                           0   \n",
       "536967                                   0                           0   \n",
       "536983                                   0                           0   \n",
       "537197                                   0                           0   \n",
       "537198                                   0                           0   \n",
       "537201                                   0                           0   \n",
       "537212                                   0                           0   \n",
       "537250                                   0                           0   \n",
       "\n",
       "Description  ZINC  STAR T-LIGHT HOLDER  ZINC BOX SIGN HOME  \\\n",
       "InvoiceNo                                                    \n",
       "536527                               0                   0   \n",
       "536840                               0                   0   \n",
       "536861                               0                   0   \n",
       "536967                               0                   0   \n",
       "536983                               0                   0   \n",
       "537197                               0                   0   \n",
       "537198                               0                   0   \n",
       "537201                               0                   0   \n",
       "537212                               0                   0   \n",
       "537250                               0                   0   \n",
       "\n",
       "Description  ZINC FOLKART SLEIGH BELLS  ZINC HEART LATTICE T-LIGHT HOLDER  \\\n",
       "InvoiceNo                                                                   \n",
       "536527                               0                                  0   \n",
       "536840                               0                                  0   \n",
       "536861                               0                                  0   \n",
       "536967                               0                                  0   \n",
       "536983                               0                                  0   \n",
       "537197                               0                                  0   \n",
       "537198                               0                                  0   \n",
       "537201                               0                                  0   \n",
       "537212                               0                                  0   \n",
       "537250                               0                                  0   \n",
       "\n",
       "Description  ZINC METAL HEART DECORATION  ZINC T-LIGHT HOLDER STAR LARGE  \\\n",
       "InvoiceNo                                                                  \n",
       "536527                                 0                               0   \n",
       "536840                                 0                               0   \n",
       "536861                                 0                               0   \n",
       "536967                                 0                               0   \n",
       "536983                                 0                               0   \n",
       "537197                                 0                               0   \n",
       "537198                                 0                               0   \n",
       "537201                                 0                               0   \n",
       "537212                                 0                               0   \n",
       "537250                                 0                               0   \n",
       "\n",
       "Description  ZINC T-LIGHT HOLDER STARS SMALL  ZINC WILLIE WINKIE  CANDLE STICK  \n",
       "InvoiceNo                                                                       \n",
       "536527                                     0                                 0  \n",
       "536840                                     0                                 0  \n",
       "536861                                     0                                 0  \n",
       "536967                                     0                                 0  \n",
       "536983                                     0                                 0  \n",
       "537197                                     0                                 0  \n",
       "537198                                     0                                 0  \n",
       "537201                                     0                                 0  \n",
       "537212                                     0                                 0  \n",
       "537250                                     0                                 0  \n",
       "\n",
       "[10 rows x 1694 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def encode_units(x):\n",
    "    if x <= 0:\n",
    "        return 0\n",
    "    if x >= 1:\n",
    "        return 1\n",
    "\n",
    "basket_sets = basket.applymap(encode_units)\n",
    "basket_sets.drop('POSTAGE', inplace=True, axis=1)\n",
    "basket_sets.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter the following code to view sets of items with at least 2 percent support.\n",
    "The visualization of this is after a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.245077</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.157549</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.137856</td>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.137856</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0.131291</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      support                                           itemsets\n",
       "178  0.245077               (ROUND SNACK BOXES SET OF4 WOODLAND)\n",
       "176  0.157549                (ROUND SNACK BOXES SET OF 4 FRUITS)\n",
       "137  0.137856                 (PLASTERS IN TIN WOODLAND ANIMALS)\n",
       "165  0.137856                         (REGENCY CAKESTAND 3 TIER)\n",
       "441  0.131291  (ROUND SNACK BOXES SET OF4 WOODLAND, ROUND SNA..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(basket_sets, min_support= 0.02, use_colnames=True)\n",
    "frequent_itemsets.sort_values('support', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the key sets of items identified, I can now apply the association rules library to discover customers’ purchase behaviors with the results sorted by `Lift`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 738 rules\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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(DOLLY GIRL CHILDRENS CUP)</td>\n",
       "      <td>(DOLLY GIRL CHILDRENS BOWL)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.083333</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>(DOLLY GIRL CHILDRENS BOWL)</td>\n",
       "      <td>(DOLLY GIRL CHILDRENS CUP)</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>38.083333</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>11.711160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>(BLUE VINTAGE SPOT BEAKER)</td>\n",
       "      <td>(PINK VINTAGE SPOT BEAKER)</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>32.642857</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>4.554340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>(PINK VINTAGE SPOT BEAKER)</td>\n",
       "      <td>(BLUE VINTAGE SPOT BEAKER)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.642857</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>2.914661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(BLUE OWL SOFT TOY)</td>\n",
       "      <td>(PINK OWL SOFT TOY)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>2.914661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>4.191101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(PINK OWL SOFT TOY)</td>\n",
       "      <td>(BLUE OWL SOFT TOY)</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>4.191101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>(BLUE VINTAGE SPOT BEAKER)</td>\n",
       "      <td>(RED VINTAGE SPOT BEAKER)</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>23.316327</td>\n",
       "      <td>0.020943</td>\n",
       "      <td>3.392779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>(RED VINTAGE SPOT BEAKER)</td>\n",
       "      <td>(BLUE VINTAGE SPOT BEAKER)</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>23.316327</td>\n",
       "      <td>0.020943</td>\n",
       "      <td>3.392779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>(WHITE SPOT BLUE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(BLUE SPOT CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.035011</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>21.971154</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>2.590810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>(BLUE SPOT CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(WHITE SPOT BLUE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.035011</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>21.971154</td>\n",
       "      <td>0.020886</td>\n",
       "      <td>4.181619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>(SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>21.866029</td>\n",
       "      <td>0.020881</td>\n",
       "      <td>2.060297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>21.866029</td>\n",
       "      <td>0.020881</td>\n",
       "      <td>10.542670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>(RED SPOT CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>21.761905</td>\n",
       "      <td>0.020876</td>\n",
       "      <td>2.908096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>(RED SPOT CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>21.761905</td>\n",
       "      <td>0.020876</td>\n",
       "      <td>3.385120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>(SET/10 BLUE POLKADOT PARTY CANDLES)</td>\n",
       "      <td>(SET/10 PINK POLKADOT PARTY CANDLES)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>21.505882</td>\n",
       "      <td>0.025037</td>\n",
       "      <td>4.814004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>(SET/10 PINK POLKADOT PARTY CANDLES)</td>\n",
       "      <td>(SET/10 BLUE POLKADOT PARTY CANDLES)</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>21.505882</td>\n",
       "      <td>0.025037</td>\n",
       "      <td>3.288403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>20.444737</td>\n",
       "      <td>0.035380</td>\n",
       "      <td>6.389497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>20.444737</td>\n",
       "      <td>0.035380</td>\n",
       "      <td>9.084245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>(RED RETROSPOT UMBRELLA)</td>\n",
       "      <td>(EDWARDIAN PARASOL RED)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>20.311111</td>\n",
       "      <td>0.020805</td>\n",
       "      <td>2.901532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>(EDWARDIAN PARASOL RED)</td>\n",
       "      <td>(RED RETROSPOT UMBRELLA)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>20.311111</td>\n",
       "      <td>0.020805</td>\n",
       "      <td>2.901532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>(WHITE SPOT RED CERAMIC DRAWER KNOB, BLUE STRI...</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>19.783550</td>\n",
       "      <td>0.020776</td>\n",
       "      <td>10.494530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(WHITE SPOT RED CERAMIC DRAWER KNOB, BLUE STRI...</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>19.783550</td>\n",
       "      <td>0.020776</td>\n",
       "      <td>1.863139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL, ROUND SNACK BOXES SE...</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>19.041667</td>\n",
       "      <td>0.020733</td>\n",
       "      <td>1.947484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL, ROUND SNACK BOXES SE...</td>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>19.041667</td>\n",
       "      <td>0.020733</td>\n",
       "      <td>5.737418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>18.413919</td>\n",
       "      <td>0.022763</td>\n",
       "      <td>2.040263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>18.413919</td>\n",
       "      <td>0.022763</td>\n",
       "      <td>6.201313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(RED SPOT CERAMIC DRAWER KNOB, WHITE SPOT RED ...</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>18.134921</td>\n",
       "      <td>0.020675</td>\n",
       "      <td>1.858962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>(RED SPOT CERAMIC DRAWER KNOB, WHITE SPOT RED ...</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>18.134921</td>\n",
       "      <td>0.020675</td>\n",
       "      <td>5.724289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>(RED TOADSTOOL LED NIGHT LIGHT)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>0.096280</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.586806</td>\n",
       "      <td>0.008901</td>\n",
       "      <td>1.123268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>(RED TOADSTOOL LED NIGHT LIGHT)</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.096280</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.152778</td>\n",
       "      <td>1.586806</td>\n",
       "      <td>0.008901</td>\n",
       "      <td>1.066686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(FAWN BLUE HOT WATER BOTTLE)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.063457</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.098214</td>\n",
       "      <td>1.547722</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>1.038542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>(FAWN BLUE HOT WATER BOTTLE)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.063457</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>1.547722</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>1.216265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>(SET OF 3 CAKE TINS PANTRY DESIGN)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.059081</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>1.511243</td>\n",
       "      <td>0.007402</td>\n",
       "      <td>1.198996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(SET OF 3 CAKE TINS PANTRY DESIGN)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.059081</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>1.511243</td>\n",
       "      <td>0.007402</td>\n",
       "      <td>1.033166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(JUMBO BAG WOODLAND ANIMALS)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.100656</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.151786</td>\n",
       "      <td>1.507958</td>\n",
       "      <td>0.012531</td>\n",
       "      <td>1.060279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>(JUMBO BAG WOODLAND ANIMALS)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.100656</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>1.507958</td>\n",
       "      <td>0.012531</td>\n",
       "      <td>1.197465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.206349</td>\n",
       "      <td>1.496851</td>\n",
       "      <td>0.009442</td>\n",
       "      <td>1.086302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.206349</td>\n",
       "      <td>1.496851</td>\n",
       "      <td>0.009442</td>\n",
       "      <td>1.086302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>(PLASTERS IN TIN CIRCUS PARADE)</td>\n",
       "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
       "      <td>0.115974</td>\n",
       "      <td>0.126915</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>1.486662</td>\n",
       "      <td>0.007163</td>\n",
       "      <td>1.076128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
       "      <td>(PLASTERS IN TIN CIRCUS PARADE)</td>\n",
       "      <td>0.126915</td>\n",
       "      <td>0.115974</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>1.486662</td>\n",
       "      <td>0.007163</td>\n",
       "      <td>1.068198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>0.102845</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.234043</td>\n",
       "      <td>1.485520</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>1.099866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.102845</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.152778</td>\n",
       "      <td>1.485520</td>\n",
       "      <td>0.007867</td>\n",
       "      <td>1.058937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.107221</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.158730</td>\n",
       "      <td>1.480402</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>1.061228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY)</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>0.107221</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.204082</td>\n",
       "      <td>1.480402</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>1.083207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>(RETROSPOT PARTY BAG + STICKER SET)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.061269</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>1.457270</td>\n",
       "      <td>0.006866</td>\n",
       "      <td>1.174325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(RETROSPOT PARTY BAG + STICKER SET)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.061269</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>1.457270</td>\n",
       "      <td>0.006866</td>\n",
       "      <td>1.030763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.102845</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.035011</td>\n",
       "      <td>0.340426</td>\n",
       "      <td>1.389058</td>\n",
       "      <td>0.009806</td>\n",
       "      <td>1.144561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(6 RIBBONS RUSTIC CHARM)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.102845</td>\n",
       "      <td>0.035011</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.389058</td>\n",
       "      <td>0.009806</td>\n",
       "      <td>1.046681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.126915</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>1.375753</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>1.057776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>0.126915</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.189655</td>\n",
       "      <td>1.375753</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>1.063923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.070022</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>1.275112</td>\n",
       "      <td>0.004721</td>\n",
       "      <td>1.021152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.070022</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>1.275112</td>\n",
       "      <td>0.004721</td>\n",
       "      <td>1.098070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>1.165816</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>1.056893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.039387</td>\n",
       "      <td>0.160714</td>\n",
       "      <td>1.165816</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>1.027236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>(PACK OF 72 RETROSPOT CAKE CASES)</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.085339</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.098214</td>\n",
       "      <td>1.150870</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>1.014277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>(PACK OF 72 RETROSPOT CAKE CASES)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND)</td>\n",
       "      <td>0.085339</td>\n",
       "      <td>0.245077</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.282051</td>\n",
       "      <td>1.150870</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>1.051500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>1.108245</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>1.020662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF 4 FRUITS)</td>\n",
       "      <td>(REGENCY CAKESTAND 3 TIER)</td>\n",
       "      <td>0.157549</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.152778</td>\n",
       "      <td>1.108245</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>1.017613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>738 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "127                         (DOLLY GIRL CHILDRENS CUP)   \n",
       "126                        (DOLLY GIRL CHILDRENS BOWL)   \n",
       "69                          (BLUE VINTAGE SPOT BEAKER)   \n",
       "68                          (PINK VINTAGE SPOT BEAKER)   \n",
       "478  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...   \n",
       "44                                 (BLUE OWL SOFT TOY)   \n",
       "483                  (BLUE STRIPE CERAMIC DRAWER KNOB)   \n",
       "45                                 (PINK OWL SOFT TOY)   \n",
       "70                          (BLUE VINTAGE SPOT BEAKER)   \n",
       "71                           (RED VINTAGE SPOT BEAKER)   \n",
       "61               (WHITE SPOT BLUE CERAMIC DRAWER KNOB)   \n",
       "60                     (BLUE SPOT CERAMIC DRAWER KNOB)   \n",
       "710                          (SPACEBOY CHILDRENS BOWL)   \n",
       "707  (SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...   \n",
       "677  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...   \n",
       "680                     (RED SPOT CERAMIC DRAWER KNOB)   \n",
       "431               (SET/10 BLUE POLKADOT PARTY CANDLES)   \n",
       "430               (SET/10 PINK POLKADOT PARTY CANDLES)   \n",
       "446                           (SPACEBOY CHILDRENS CUP)   \n",
       "447                          (SPACEBOY CHILDRENS BOWL)   \n",
       "129                           (RED RETROSPOT UMBRELLA)   \n",
       "128                            (EDWARDIAN PARASOL RED)   \n",
       "480  (WHITE SPOT RED CERAMIC DRAWER KNOB, BLUE STRI...   \n",
       "481                   (RED STRIPE CERAMIC DRAWER KNOB)   \n",
       "709                           (SPACEBOY CHILDRENS CUP)   \n",
       "708  (SPACEBOY CHILDRENS BOWL, ROUND SNACK BOXES SE...   \n",
       "64                    (RED STRIPE CERAMIC DRAWER KNOB)   \n",
       "65                   (BLUE STRIPE CERAMIC DRAWER KNOB)   \n",
       "679                   (RED STRIPE CERAMIC DRAWER KNOB)   \n",
       "678  (RED SPOT CERAMIC DRAWER KNOB, WHITE SPOT RED ...   \n",
       "..                                                 ...   \n",
       "348                    (RED TOADSTOOL LED NIGHT LIGHT)   \n",
       "349                (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
       "131               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "130                       (FAWN BLUE HOT WATER BOTTLE)   \n",
       "394                 (SET OF 3 CAKE TINS PANTRY DESIGN)   \n",
       "395               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "199               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "198                       (JUMBO BAG WOODLAND ANIMALS)   \n",
       "293                         (REGENCY CAKESTAND 3 TIER)   \n",
       "292                 (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
       "266                    (PLASTERS IN TIN CIRCUS PARADE)   \n",
       "267                           (WOODLAND CHARLOTTE BAG)   \n",
       "8                             (6 RIBBONS RUSTIC CHARM)   \n",
       "9                  (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
       "272                         (REGENCY CAKESTAND 3 TIER)   \n",
       "273                         (PLASTERS IN TIN SPACEBOY)   \n",
       "366                (RETROSPOT PARTY BAG + STICKER SET)   \n",
       "367               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "10                            (6 RIBBONS RUSTIC CHARM)   \n",
       "11                (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "364                         (REGENCY CAKESTAND 3 TIER)   \n",
       "365                           (WOODLAND CHARLOTTE BAG)   \n",
       "34                (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "35                         (ALARM CLOCK BAKELIKE PINK)   \n",
       "359                         (REGENCY CAKESTAND 3 TIER)   \n",
       "358               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "239               (ROUND SNACK BOXES SET OF4 WOODLAND)   \n",
       "238                  (PACK OF 72 RETROSPOT CAKE CASES)   \n",
       "357                         (REGENCY CAKESTAND 3 TIER)   \n",
       "356                (ROUND SNACK BOXES SET OF 4 FRUITS)   \n",
       "\n",
       "                                           consequents  antecedent support  \\\n",
       "127                        (DOLLY GIRL CHILDRENS BOWL)            0.024070   \n",
       "126                         (DOLLY GIRL CHILDRENS CUP)            0.026258   \n",
       "69                          (PINK VINTAGE SPOT BEAKER)            0.030635   \n",
       "68                          (BLUE VINTAGE SPOT BEAKER)            0.024070   \n",
       "478                  (BLUE STRIPE CERAMIC DRAWER KNOB)            0.032823   \n",
       "44                                 (PINK OWL SOFT TOY)            0.032823   \n",
       "483  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...            0.028446   \n",
       "45                                 (BLUE OWL SOFT TOY)            0.028446   \n",
       "70                           (RED VINTAGE SPOT BEAKER)            0.030635   \n",
       "71                          (BLUE VINTAGE SPOT BEAKER)            0.030635   \n",
       "61                     (BLUE SPOT CERAMIC DRAWER KNOB)            0.035011   \n",
       "60               (WHITE SPOT BLUE CERAMIC DRAWER KNOB)            0.028446   \n",
       "710  (SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...            0.041575   \n",
       "707                          (SPACEBOY CHILDRENS BOWL)            0.024070   \n",
       "677                     (RED SPOT CERAMIC DRAWER KNOB)            0.032823   \n",
       "680  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...            0.030635   \n",
       "431               (SET/10 PINK POLKADOT PARTY CANDLES)            0.032823   \n",
       "430               (SET/10 BLUE POLKADOT PARTY CANDLES)            0.037199   \n",
       "446                          (SPACEBOY CHILDRENS BOWL)            0.043764   \n",
       "447                           (SPACEBOY CHILDRENS CUP)            0.041575   \n",
       "129                            (EDWARDIAN PARASOL RED)            0.032823   \n",
       "128                           (RED RETROSPOT UMBRELLA)            0.032823   \n",
       "480                   (RED STRIPE CERAMIC DRAWER KNOB)            0.024070   \n",
       "481  (WHITE SPOT RED CERAMIC DRAWER KNOB, BLUE STRI...            0.045952   \n",
       "709  (SPACEBOY CHILDRENS BOWL, ROUND SNACK BOXES SE...            0.043764   \n",
       "708                           (SPACEBOY CHILDRENS CUP)            0.026258   \n",
       "64                   (BLUE STRIPE CERAMIC DRAWER KNOB)            0.045952   \n",
       "65                    (RED STRIPE CERAMIC DRAWER KNOB)            0.028446   \n",
       "679  (RED SPOT CERAMIC DRAWER KNOB, WHITE SPOT RED ...            0.045952   \n",
       "678                   (RED STRIPE CERAMIC DRAWER KNOB)            0.026258   \n",
       "..                                                 ...                 ...   \n",
       "348                (ROUND SNACK BOXES SET OF 4 FRUITS)            0.096280   \n",
       "349                    (RED TOADSTOOL LED NIGHT LIGHT)            0.157549   \n",
       "131                       (FAWN BLUE HOT WATER BOTTLE)            0.245077   \n",
       "130               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.063457   \n",
       "394               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.059081   \n",
       "395                 (SET OF 3 CAKE TINS PANTRY DESIGN)            0.245077   \n",
       "199                       (JUMBO BAG WOODLAND ANIMALS)            0.245077   \n",
       "198               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.100656   \n",
       "293                 (PLASTERS IN TIN WOODLAND ANIMALS)            0.137856   \n",
       "292                         (REGENCY CAKESTAND 3 TIER)            0.137856   \n",
       "266                           (WOODLAND CHARLOTTE BAG)            0.115974   \n",
       "267                    (PLASTERS IN TIN CIRCUS PARADE)            0.126915   \n",
       "8                  (ROUND SNACK BOXES SET OF 4 FRUITS)            0.102845   \n",
       "9                             (6 RIBBONS RUSTIC CHARM)            0.157549   \n",
       "272                         (PLASTERS IN TIN SPACEBOY)            0.137856   \n",
       "273                         (REGENCY CAKESTAND 3 TIER)            0.107221   \n",
       "366               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.061269   \n",
       "367                (RETROSPOT PARTY BAG + STICKER SET)            0.245077   \n",
       "10                (ROUND SNACK BOXES SET OF4 WOODLAND)            0.102845   \n",
       "11                            (6 RIBBONS RUSTIC CHARM)            0.245077   \n",
       "364                           (WOODLAND CHARLOTTE BAG)            0.137856   \n",
       "365                         (REGENCY CAKESTAND 3 TIER)            0.126915   \n",
       "34                         (ALARM CLOCK BAKELIKE PINK)            0.245077   \n",
       "35                (ROUND SNACK BOXES SET OF4 WOODLAND)            0.070022   \n",
       "359               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.137856   \n",
       "358                         (REGENCY CAKESTAND 3 TIER)            0.245077   \n",
       "239                  (PACK OF 72 RETROSPOT CAKE CASES)            0.245077   \n",
       "238               (ROUND SNACK BOXES SET OF4 WOODLAND)            0.085339   \n",
       "357                (ROUND SNACK BOXES SET OF 4 FRUITS)            0.137856   \n",
       "356                         (REGENCY CAKESTAND 3 TIER)            0.157549   \n",
       "\n",
       "     consequent support   support  confidence       lift  leverage  conviction  \n",
       "127            0.026258  0.024070    1.000000  38.083333  0.023438         inf  \n",
       "126            0.024070  0.024070    0.916667  38.083333  0.023438   11.711160  \n",
       "69             0.024070  0.024070    0.785714  32.642857  0.023333    4.554340  \n",
       "68             0.030635  0.024070    1.000000  32.642857  0.023333         inf  \n",
       "478            0.028446  0.021882    0.666667  23.435897  0.020948    2.914661  \n",
       "44             0.028446  0.021882    0.666667  23.435897  0.020948    2.914661  \n",
       "483            0.032823  0.021882    0.769231  23.435897  0.020948    4.191101  \n",
       "45             0.032823  0.021882    0.769231  23.435897  0.020948    4.191101  \n",
       "70             0.030635  0.021882    0.714286  23.316327  0.020943    3.392779  \n",
       "71             0.030635  0.021882    0.714286  23.316327  0.020943    3.392779  \n",
       "61             0.028446  0.021882    0.625000  21.971154  0.020886    2.590810  \n",
       "60             0.035011  0.021882    0.769231  21.971154  0.020886    4.181619  \n",
       "710            0.024070  0.021882    0.526316  21.866029  0.020881    2.060297  \n",
       "707            0.041575  0.021882    0.909091  21.866029  0.020881   10.542670  \n",
       "677            0.030635  0.021882    0.666667  21.761905  0.020876    2.908096  \n",
       "680            0.032823  0.021882    0.714286  21.761905  0.020876    3.385120  \n",
       "431            0.037199  0.026258    0.800000  21.505882  0.025037    4.814004  \n",
       "430            0.032823  0.026258    0.705882  21.505882  0.025037    3.288403  \n",
       "446            0.041575  0.037199    0.850000  20.444737  0.035380    6.389497  \n",
       "447            0.043764  0.037199    0.894737  20.444737  0.035380    9.084245  \n",
       "129            0.032823  0.021882    0.666667  20.311111  0.020805    2.901532  \n",
       "128            0.032823  0.021882    0.666667  20.311111  0.020805    2.901532  \n",
       "480            0.045952  0.021882    0.909091  19.783550  0.020776   10.494530  \n",
       "481            0.024070  0.021882    0.476190  19.783550  0.020776    1.863139  \n",
       "709            0.026258  0.021882    0.500000  19.041667  0.020733    1.947484  \n",
       "708            0.043764  0.021882    0.833333  19.041667  0.020733    5.737418  \n",
       "64             0.028446  0.024070    0.523810  18.413919  0.022763    2.040263  \n",
       "65             0.045952  0.024070    0.846154  18.413919  0.022763    6.201313  \n",
       "679            0.026258  0.021882    0.476190  18.134921  0.020675    1.858962  \n",
       "678            0.045952  0.021882    0.833333  18.134921  0.020675    5.724289  \n",
       "..                  ...       ...         ...        ...       ...         ...  \n",
       "348            0.157549  0.024070    0.250000   1.586806  0.008901    1.123268  \n",
       "349            0.096280  0.024070    0.152778   1.586806  0.008901    1.066686  \n",
       "131            0.063457  0.024070    0.098214   1.547722  0.008518    1.038542  \n",
       "130            0.245077  0.024070    0.379310   1.547722  0.008518    1.216265  \n",
       "394            0.245077  0.021882    0.370370   1.511243  0.007402    1.198996  \n",
       "395            0.059081  0.021882    0.089286   1.511243  0.007402    1.033166  \n",
       "199            0.100656  0.037199    0.151786   1.507958  0.012531    1.060279  \n",
       "198            0.245077  0.037199    0.369565   1.507958  0.012531    1.197465  \n",
       "293            0.137856  0.028446    0.206349   1.496851  0.009442    1.086302  \n",
       "292            0.137856  0.028446    0.206349   1.496851  0.009442    1.086302  \n",
       "266            0.126915  0.021882    0.188679   1.486662  0.007163    1.076128  \n",
       "267            0.115974  0.021882    0.172414   1.486662  0.007163    1.068198  \n",
       "8              0.157549  0.024070    0.234043   1.485520  0.007867    1.099866  \n",
       "9              0.102845  0.024070    0.152778   1.485520  0.007867    1.058937  \n",
       "272            0.107221  0.021882    0.158730   1.480402  0.007101    1.061228  \n",
       "273            0.137856  0.021882    0.204082   1.480402  0.007101    1.083207  \n",
       "366            0.245077  0.021882    0.357143   1.457270  0.006866    1.174325  \n",
       "367            0.061269  0.021882    0.089286   1.457270  0.006866    1.030763  \n",
       "10             0.245077  0.035011    0.340426   1.389058  0.009806    1.144561  \n",
       "11             0.102845  0.035011    0.142857   1.389058  0.009806    1.046681  \n",
       "364            0.126915  0.024070    0.174603   1.375753  0.006574    1.057776  \n",
       "365            0.137856  0.024070    0.189655   1.375753  0.006574    1.063923  \n",
       "34             0.070022  0.021882    0.089286   1.275112  0.004721    1.021152  \n",
       "35             0.245077  0.021882    0.312500   1.275112  0.004721    1.098070  \n",
       "359            0.245077  0.039387    0.285714   1.165816  0.005602    1.056893  \n",
       "358            0.137856  0.039387    0.160714   1.165816  0.005602    1.027236  \n",
       "239            0.085339  0.024070    0.098214   1.150870  0.003155    1.014277  \n",
       "238            0.245077  0.024070    0.282051   1.150870  0.003155    1.051500  \n",
       "357            0.157549  0.024070    0.174603   1.108245  0.002351    1.020662  \n",
       "356            0.137856  0.024070    0.152778   1.108245  0.002351    1.017613  \n",
       "\n",
       "[738 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "print (\"Generated {:,} rules\".format(len(rules)))\n",
    "rules.sort_values('lift',ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results, sorted by `Lift` reveal the purchasing patterns of German customers on the site. We can see that customers who buy `DOLLY GIRL CHILDRENS BOWL` also purchase `DOLLY GIRL CHILDRENS CUP` and other similar pair of items. As far as actionable insights go, it could be recommended to the site owners to offer bundle deals on these items, keeping in mind that this list is sorted by `Lift` and not `occurrence`. It may not make sense to introduce a new bundle or product offering if it isn’t popular enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>738.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>738.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.028067</td>\n",
       "      <td>0.408796</td>\n",
       "      <td>5.786787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.009774</td>\n",
       "      <td>0.231677</td>\n",
       "      <td>5.338703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>1.108245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.207547</td>\n",
       "      <td>2.493552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3.554444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.551293</td>\n",
       "      <td>6.566092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.131291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          support  confidence        lift\n",
       "count  738.000000  738.000000  738.000000\n",
       "mean     0.028067    0.408796    5.786787\n",
       "std      0.009774    0.231677    5.338703\n",
       "min      0.021882    0.089286    1.108245\n",
       "25%      0.021882    0.207547    2.493552\n",
       "50%      0.024070    0.375000    3.554444\n",
       "75%      0.030635    0.551293    6.566092\n",
       "max      0.131291    1.000000   38.083333"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get stats on objective measures\n",
    "rules[[\"support\",\"confidence\", \"lift\"]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we see summary statistics for the rule quality measures: support, confidence, and lift. Support and confidence should not be very surprising, since we used these as selection criteria for the rules. However, we might be alarmed if most or all of the rules were very near the minimum thresholds—not the case here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter the dataframe using standard pandas code. In this case, look for a large lift (20) and high confidence (.8):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>(PINK VINTAGE SPOT BEAKER)</td>\n",
       "      <td>(BLUE VINTAGE SPOT BEAKER)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.642857</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>(DOLLY GIRL CHILDRENS BOWL)</td>\n",
       "      <td>(DOLLY GIRL CHILDRENS CUP)</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>38.083333</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>11.711160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(DOLLY GIRL CHILDRENS CUP)</td>\n",
       "      <td>(DOLLY GIRL CHILDRENS BOWL)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.083333</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>(SET/10 BLUE POLKADOT PARTY CANDLES)</td>\n",
       "      <td>(SET/10 PINK POLKADOT PARTY CANDLES)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>21.505882</td>\n",
       "      <td>0.025037</td>\n",
       "      <td>4.814004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>20.444737</td>\n",
       "      <td>0.035380</td>\n",
       "      <td>6.389497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>(SPACEBOY CHILDRENS CUP)</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.043764</td>\n",
       "      <td>0.037199</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>20.444737</td>\n",
       "      <td>0.035380</td>\n",
       "      <td>9.084245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>21.866029</td>\n",
       "      <td>0.020881</td>\n",
       "      <td>10.542670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "68                          (PINK VINTAGE SPOT BEAKER)   \n",
       "126                        (DOLLY GIRL CHILDRENS BOWL)   \n",
       "127                         (DOLLY GIRL CHILDRENS CUP)   \n",
       "431               (SET/10 BLUE POLKADOT PARTY CANDLES)   \n",
       "446                           (SPACEBOY CHILDRENS CUP)   \n",
       "447                          (SPACEBOY CHILDRENS BOWL)   \n",
       "707  (SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...   \n",
       "\n",
       "                              consequents  antecedent support  \\\n",
       "68             (BLUE VINTAGE SPOT BEAKER)            0.024070   \n",
       "126            (DOLLY GIRL CHILDRENS CUP)            0.026258   \n",
       "127           (DOLLY GIRL CHILDRENS BOWL)            0.024070   \n",
       "431  (SET/10 PINK POLKADOT PARTY CANDLES)            0.032823   \n",
       "446             (SPACEBOY CHILDRENS BOWL)            0.043764   \n",
       "447              (SPACEBOY CHILDRENS CUP)            0.041575   \n",
       "707             (SPACEBOY CHILDRENS BOWL)            0.024070   \n",
       "\n",
       "     consequent support   support  confidence       lift  leverage  conviction  \n",
       "68             0.030635  0.024070    1.000000  32.642857  0.023333         inf  \n",
       "126            0.024070  0.024070    0.916667  38.083333  0.023438   11.711160  \n",
       "127            0.026258  0.024070    1.000000  38.083333  0.023438         inf  \n",
       "431            0.037199  0.026258    0.800000  21.505882  0.025037    4.814004  \n",
       "446            0.041575  0.037199    0.850000  20.444737  0.035380    6.389497  \n",
       "447            0.043764  0.037199    0.894737  20.444737  0.035380    9.084245  \n",
       "707            0.041575  0.021882    0.909091  21.866029  0.020881   10.542670  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[ (rules['lift'] >= 20) &\n",
    "       (rules['confidence'] >= 0.8) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the rules, it seems that the `PINK VINTAGE SPOT BEAKER` and `BLUE VINTAGE SPOT BEAKER` are purchased together in a manner that is higher than the overall probability would suggest.\n",
    "\n",
    "At this point, we can want to look at how much opportunity there is to use the popularity of one product to drive sales of another. For instance, we can see that we sell 104 `PINK VINTAGE SPOT BEAKER`  and 136 `BLUE VINTAGE SPOT BEAKER`. So maybe we can drive more `PINK VINTAGE SPOT BEAKER` sales through recommendations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket['PINK VINTAGE SPOT BEAKER'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket['BLUE VINTAGE SPOT BEAKER'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# add new columnn storing the rule length\n",
    "rules[\"rule_len\"] = rules.apply(lambda row: len(row[\"antecedents\"])+len(row[\"consequents\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>rule_len</th>\n",
       "      <th colspan=\"2\" halign=\"left\">support</th>\n",
       "      <th colspan=\"2\" halign=\"left\">lift</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.029303</td>\n",
       "      <td>460</td>\n",
       "      <td>5.777915</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.026010</td>\n",
       "      <td>264</td>\n",
       "      <td>5.824470</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.026258</td>\n",
       "      <td>14</td>\n",
       "      <td>5.367698</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rule_len   support            lift      \n",
       "                mean count      mean count\n",
       "0        2  0.029303   460  5.777915   460\n",
       "1        3  0.026010   264  5.824470   264\n",
       "2        4  0.026258    14  5.367698    14"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get stats on rules groupt by rule length\n",
    "rules[[\"rule_len\",\"support\", \"lift\"]].groupby(\"rule_len\").agg(['mean', 'count']).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restricting analysis to rules of length 3 and order rules by lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>rule_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>2.914661</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>(BLUE STRIPE CERAMIC DRAWER KNOB)</td>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>0.028446</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>23.435897</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>4.191101</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>(SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>21.866029</td>\n",
       "      <td>0.020881</td>\n",
       "      <td>2.060297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>(SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...</td>\n",
       "      <td>(SPACEBOY CHILDRENS BOWL)</td>\n",
       "      <td>0.024070</td>\n",
       "      <td>0.041575</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>21.866029</td>\n",
       "      <td>0.020881</td>\n",
       "      <td>10.542670</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>(RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...</td>\n",
       "      <td>(RED SPOT CERAMIC DRAWER KNOB)</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.030635</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>21.761905</td>\n",
       "      <td>0.020876</td>\n",
       "      <td>2.908096</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "478  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...   \n",
       "483                  (BLUE STRIPE CERAMIC DRAWER KNOB)   \n",
       "710                          (SPACEBOY CHILDRENS BOWL)   \n",
       "707  (SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...   \n",
       "677  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...   \n",
       "\n",
       "                                           consequents  antecedent support  \\\n",
       "478                  (BLUE STRIPE CERAMIC DRAWER KNOB)            0.032823   \n",
       "483  (RED STRIPE CERAMIC DRAWER KNOB, WHITE SPOT RE...            0.028446   \n",
       "710  (SPACEBOY CHILDRENS CUP, ROUND SNACK BOXES SET...            0.041575   \n",
       "707                          (SPACEBOY CHILDRENS BOWL)            0.024070   \n",
       "677                     (RED SPOT CERAMIC DRAWER KNOB)            0.032823   \n",
       "\n",
       "     consequent support   support  confidence       lift  leverage  \\\n",
       "478            0.028446  0.021882    0.666667  23.435897  0.020948   \n",
       "483            0.032823  0.021882    0.769231  23.435897  0.020948   \n",
       "710            0.024070  0.021882    0.526316  21.866029  0.020881   \n",
       "707            0.041575  0.021882    0.909091  21.866029  0.020881   \n",
       "677            0.030635  0.021882    0.666667  21.761905  0.020876   \n",
       "\n",
       "     conviction  rule_len  \n",
       "478    2.914661         3  \n",
       "483    4.191101         3  \n",
       "710    2.060297         3  \n",
       "707   10.542670         3  \n",
       "677    2.908096         3  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules[\"rule_len\"]==3].sort_values(by='lift',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the rules, it seems that the `ROUND SNACK BOXES SET OF4 WOODLAND , RED RETR...` and `WOODLAND CHARLOTTE BAG` are purchased together in a manner that is higher than the overall probability would suggest.\n",
    "\n",
    "At this point, we could look at how much opportunity there is to use the popularity of one product to drive sales of another. For instance, if the sales of  `ROUND SNACK BOXES SET OF4 WOODLAND , RED RETR...` are decreasing then maybe we can drive more `CHARLOTTE SUKI DESIGN` bag sales through recommendations?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent association rules as diagram, NetworkX python library is utilized\n",
    "Code sourced from : http://intelligentonlinetools.com/blog/2018/02/10/how-to-create-data-visualization-for-association-rules-in-data-mining/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def draw_graph(rules, rules_to_show):\n",
    "  import networkx as nx  \n",
    "  G1 = nx.DiGraph()\n",
    "   \n",
    "  color_map=[]\n",
    "  N = 50\n",
    "  colors = np.random.rand(N)    \n",
    "  strs=['R0', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11']   \n",
    "   \n",
    "   \n",
    "  for i in range (rules_to_show):      \n",
    "    G1.add_nodes_from([\"R\"+str(i)])\n",
    "    \n",
    "     \n",
    "    for a in rules.iloc[i]['antecedents']:\n",
    "                \n",
    "        G1.add_nodes_from([a])\n",
    "        \n",
    "        G1.add_edge(a, \"R\"+str(i), color=colors[i] , weight = 2)\n",
    "       \n",
    "    for c in rules.iloc[i]['consequents']:\n",
    "             \n",
    "            G1.add_nodes_from([c])\n",
    "            \n",
    "            G1.add_edge(\"R\"+str(i), c, color=colors[i],  weight=2)\n",
    " \n",
    "  for node in G1:\n",
    "       found_a_string = False\n",
    "       for item in strs: \n",
    "           if node==item:\n",
    "                found_a_string = True\n",
    "       if found_a_string:\n",
    "            color_map.append('yellow')\n",
    "       else:\n",
    "            color_map.append('green')       \n",
    " \n",
    " \n",
    "   \n",
    "  edges = G1.edges()\n",
    "  colors = [G1[u][v]['color'] for u,v in edges]\n",
    "  weights = [G1[u][v]['weight'] for u,v in edges]\n",
    " \n",
    "  pos = nx.spring_layout(G1, k=16, scale=1)\n",
    "  nx.draw(G1, pos, edges=edges, node_color = color_map, edge_color=colors, width=weights, font_size=16, with_labels=False)            \n",
    "   \n",
    "  for p in pos:  # raise text positions\n",
    "           pos[p][1] += 0.07\n",
    "  nx.draw_networkx_labels(G1, pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Directed graph below is built for this rule. Arrows are drawn as just thicker blue stubs. The node with R0 identifies one rule, and it will have always incoming and outcoming edges. Incoming edge(s) will represent antecedants and the stub (arrow) will be next to node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graph (rules, 5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the rules distribution color mapped by Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.scatter(rules['support'], rules['confidence'], c=rules['lift'], alpha=0.9, cmap='YlOrRd');\n",
    "plt.title('Rules distribution color mapped by lift');\n",
    "plt.xlabel('Support')\n",
    "plt.ylabel('Confidence')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommender Systems\n",
    "\n",
    "We use the well-known [MovieLens dataset](http://www.grouplens.org/system/files/ml-1m.zip) (in this case the 1M version). \n",
    "\n",
    "Get detail about the dataset in the README. http://files.grouplens.org/papers/ml-1m-README.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import ceil\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\faraz\\Miniconda3\\envs\\py3\\lib\\site-packages\\ipykernel_launcher.py:1: ParserWarning:\n",
      "\n",
      "Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "\n",
      "C:\\Users\\faraz\\Miniconda3\\envs\\py3\\lib\\site-packages\\ipykernel_launcher.py:2: ParserWarning:\n",
      "\n",
      "Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "\n",
      "C:\\Users\\faraz\\Miniconda3\\envs\\py3\\lib\\site-packages\\ipykernel_launcher.py:3: ParserWarning:\n",
      "\n",
      "Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "user   = pd.read_csv('users.dat',sep=\"::\",lineterminator='\\r',header=None)\n",
    "movies = pd.read_csv('movies.dat',sep=\"::\",lineterminator='\\r',header = None)\n",
    "rating = pd.read_csv('ratings.dat',sep=\"::\",lineterminator='\\r',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1   2   3      4\n",
      "0  1  F   1  10  48067\n",
      "1  2  M  56  16  70072\n",
      "2  3  M  25  15  55117\n",
      "3  4  M  45   7  02460\n",
      "4  5  M  25  20  55455\n",
      "   0                                   1                             2\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",
      "   0     1  2          3\n",
      "0  1  1193  5  978300760\n",
      "1  1   661  3  978302109\n",
      "2  1   914  3  978301968\n",
      "3  1  3408  4  978300275\n",
      "4  1  2355  5  978824291\n"
     ]
    }
   ],
   "source": [
    "print (user.head())\n",
    "print (movies.head())\n",
    "print (rating.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "user.columns = ['UserID','Gender','Age','Occupation','Zip Code']\n",
    "movies.columns = ['MovieID','Title','Genres']\n",
    "rating.columns = ['UserID','MovieID','Rating','Timestamp (s)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['UserID', 'Gender', 'Age', 'Occupation', 'Zip Code'], dtype='object')\n",
      "Index(['MovieID', 'Title', 'Genres'], dtype='object')\n",
      "Index(['UserID', 'MovieID', 'Rating', 'Timestamp (s)'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(user.columns)\n",
    "print(movies.columns)\n",
    "print(rating.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging all the data into one DataFrame `lens`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "movie_ratings = pd.merge(movies, rating)\n",
    "lens = pd.merge(movie_ratings, user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the data a bit and get a look at some of the most rated movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "American Beauty (1999)                                   3428\n",
       "Star Wars: Episode IV - A New Hope (1977)                2991\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
       "Jurassic Park (1993)                                     2672\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated = lens.groupby('Title')['Rating'].count().sort_values(ascending=False).head()\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ratings_df = pd.DataFrame(lens.groupby('Title')['Rating'].mean())\n",
    "ratings_df['num_of_ratings'] = pd.DataFrame(lens.groupby('Title')['Rating'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d101413cf8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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_df['Rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most movies are distributed around 3.0 to 4.0 ratings.The peaks observed at some whole numbers like 1.0, 2.0  and 3.0  can be made sense as to the way people rate movies and probably these movies have fewer people watching them. We could term these as possible outliers due to their relatively fewer occurances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1d10f785438>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"Rating\", y='num_of_ratings', data=ratings_df,marginal_kws=dict(bins=60, rug=True),annot_kws=dict(stat=\"r\"), s=40, edgecolor=\"w\", linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this data, we can see that as a movie get more ratings, it is more likely for the movie to have a higher rating. It makes sense because, if a movie is good, more people may watch it and if more people watch it, it more likely to have a higher number of people rating it. Hence it also makes sense that the 1,2,5 star rated movies are lesser which could be classified as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a general idea of what the data looks like, let's move on to creating a simple recommendation system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code filters the number of Movies. Choosing a small threshold of 100 ensures that movies with that number of aggregate ratings will be considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from length: 1000209\n",
      "to length: 940925\n"
     ]
    }
   ],
   "source": [
    "minMovieRatings = 100\n",
    "filterMovies = lens['MovieID'].value_counts() > minMovieRatings\n",
    "filterMovies = filterMovies[filterMovies].index\n",
    "filterMovies = lens[lens['MovieID'].isin(filterMovies)]\n",
    "print('from length:', len(lens))\n",
    "print('to length:', len(filterMovies))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Generate the distribution of ratings\n",
    "\n",
    "First, we look at the Count of` Ratings` per `UserID` and Count of `Ratings` per `MovieID`.\n",
    "Then, we observe the difference when we use Average instead of Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count of the `Ratings` per `UserID` and the plot of this data with a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rating\n",
       "UserID        \n",
       "1           52\n",
       "2          125\n",
       "3           50\n",
       "4           21\n",
       "5          174"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_stats = pd.DataFrame(filterMovies.groupby('UserID')['Rating'].count())\n",
    "movie_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": [
    "atleast_100 = movie_stats['Rating'] >= 100\n",
    "movie_stats[atleast_100].plot.hist(bins=70)\n",
    "plt.title(\"Distribution of ratings per User\")\n",
    "plt.ylabel('count of ratings')\n",
    "plt.xlabel('Users');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count of the `Ratings` per `MovieID` and the plot of this data with a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MovieID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Rating\n",
       "MovieID        \n",
       "1          2077\n",
       "2           701\n",
       "3           478\n",
       "4           170\n",
       "5           296"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(filterMovies.groupby('MovieID')['Rating'].count())\n",
    "atleast_100 = temp['Rating'] >= 100\n",
    "filtered = temp[atleast_100]\n",
    "filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": [
    "filtered.plot.hist(bins=70)\n",
    "plt.title(\"Distribution of ratings per User\")\n",
    "plt.ylabel('count of ratings')\n",
    "plt.xlabel('Users');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average of the `Ratings` per `UserID` and the plot of this data in a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "movie_stats = pd.DataFrame(filterMovies.groupby('UserID')['Rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": [
    "movie_stats.plot.hist(bins=70)\n",
    "plt.title(\"Distribution of ratings overs Movies\")\n",
    "plt.ylabel('Count of Users')\n",
    "plt.xlabel('Ratings');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most movies are distributed around 3.0 to 4.0 ratings.The few values observed at some numbers like 1.0, 2.0 and 5.0 can be made sense as to the way people rate movies and probably these movies have fewer people watching them. We could term these as possible outliers due to their relatively fewer occurances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average of the `Ratings` per `MovieID` and the plot of this data with a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "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>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MovieID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.146846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.201141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.016736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.729412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.006757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rating\n",
       "MovieID          \n",
       "1        4.146846\n",
       "2        3.201141\n",
       "3        3.016736\n",
       "4        2.729412\n",
       "5        3.006757"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_stats = pd.DataFrame(lens.groupby('MovieID')['Rating'].mean())\n",
    "movie_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PqVEkVi0easPMzHK5BmHWyxR/0jfrKtcgzMwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHI5QZiZWS7f5mpWx/xwmtWSaxBmZpbLCcLMzHI5QZiZWS73QZhZl5Srf8T9LPXLCcKsh6vE2Esez8nACcKsrviN2eqJ+yDMzCxXJaccvVzSMkkLC9Z9S9JfJf1F0s2SBhdsO0vSIkl/k/T+SsVlZmalqWQT0xXAxcBPC9bdAZwVES2SvgGcBXxJ0ljgaGA3YATwO0lvjIh1FYzPzMoor3nMHc49WyXnpL5XUkPRut8WLN4PHJFeTwKui4hXgCclLQL2BO6rVHxmtoH7PixPLfsgPgX8Kr0eCTxdsK0prTMzsxqpSYKQdA7QAlzduipnt2jj2KmS5kqa29zcXKkQzcz6vKonCEmTgUOBYyOiNQk0ATsU7DYKWJp3fETMiIjGiGgcPnx4ZYM1M+vDqpogJB0EfAk4LCJeLNg0Gzha0uaSxgC7AA9UMzYzM9tYxTqpJV0L7A8Mk9QEnEd219LmwB2SAO6PiBMj4hFJs4BHyZqeTvYdTGZmtVXJu5iOyVk9s539LwAuqFQ8ZmbWOX6S2szMcjlBmJlZLicIMzPL5QRhZma5OkwQkvaVtFV6/XFJ35a0Y+VDMzOzWiqlBvED4EVJewBnAkvYeAA+MzPrhUq5zbUlIkLSJOB7ETEzPQ1tZlXmQfWsmkpJEKslnQUcB7xLUj+gf2XDMjOzWiuliemjwCvApyLin2SjrH6rolGZmVnNdZggUlK4kWyIDIDlwM2VDMrMzGqvwyYmSScAU4EhwM5kNYjLgImVDc3MerriPhPPMNezlNLEdDKwL/ACQET8Hdi2kkGZmVntlZIgXomIV1sXJG1KG5P5mJlZ71HKXUz3SDob2ELSgcBJwK2VDcvMKsm3y1opSqlBTAOagYeBzwC/BM6tZFBmZlZ7HdYgIuI/wI/Sl5mViT/FW71rM0FImhURR0l6mJw+h4h4S0UjMzOzmmqvBvG59P3QrpxY0uXp2GURMS6tGwJcDzQAi4GjIuJ5ZfOPfg/4APAiMCUi5nelXDMzK482+yAi4tn08sNk4zEtKfwq4dxXAAcVrZsG3BkRuwB3pmWAg4Fd0tdUsgECzcyshkrppN4G+K2kP0g6WdJ2pZw4Iu4F/lW0ehJwZXp9JXB4wfqfRuZ+YLCk7Uspx8zMKqOUoTa+GhG7kT0wN4LsttffdbG87VprJul76wN3I4GnC/ZrSuvMzKxGSnkOotUy4J/ACsr/JLVy1uU+jCdpKlkzFKNHjy5zGGZWSb5zq2cpZUa5z0q6m6zPYBhwQjfuYHqutekofV+W1jcBOxTsNwpYmneCiJgREY0R0Th8+PAuhmFmZh0ppQ9iR+DzEbFbRJwXEY92o7zZQOtkQ5OBWwrWf0KZvYFVBZ3kZmZWA6U8KDdN0h6STkmr/hARD3V0nKRrgf2BYZKagPOA6cAsSccDTwFHpt1/SXaL6yKy21w/2dkfxMzMyquU4b5PI2vzvymt+pmkGRHx/faOi4hj2tj0mmHCIyLIOsHNzKxOlNJJ/Wlgr4j4N4CkbwD3Ae0mCDMz69lKSRAC1hUsryP/riMza4fv4LGeppQE8RNgjqTWaUYPB2ZWLiQzM6sHpXRSfzvd5vpOsprDJyPiwUoHZmZmtdXeaK5DChYXp6/12yKieBgNMzPrRdqrQSwne4CtJS0X9jsEsFOlgjIzs9prL0F8n+w5hj8B1wJ/TLejmplZH9DecN+fA8YDPweOAx6U9E1JY6oVnJmZ1U67Q22k4bfvAs4ELiN7wvm91QjMzMxqq71O6q3I5mn4KDCc7EnqCRHxdFvHmJlZ79FeH8Qy4O9k/Q+LyDqm3y7p7QARcVM7x5r1GMUPsC2efkiNIjGrL+0liJ+TJYU3pa9CwYaxmczMrBdqM0FExJQqxmFmZnWmlPkgzMysD3KCMDOzXG0mCElHpu9+7sHMrA9qrwZxVvp+YzUCMTOz+tLeXUwrJN0FjJE0u3hjRBzW1UIl/RfZREQBPEz2AN72wHXAEGA+cFxEvNrVMswqybfGWl/QXoI4BJgAXAX8b7kKlDQSOA0YGxEvSZoFHE02J/V3IuI6SZcBxwM/KFe5Zt3hyX6sL2rvNtdXgfslvSMimiUNzFbHmjKVu4WktcCWwLPAAcDH0vYrgfNxgjAzq5lS7mLaTtKDwELgUUnzJI3raoER8QxwIfAUWWJYBcwDVkZE69DiTcDIrpZhZmbdV0qCmAGcHhE7RsRo4Iy0rkskvY5sjKcxwAhgK+DgnF1zhxaXNFXSXElzm5ubuxqGmZl1oJQ5qbdKI7oCEBF3p4H8uuq9wJMR0Qwg6SbgHcBgSZumWsQoYGnewRExg5SgGhsbPT+FtSuv78AdymalKaUG8YSkL0tqSF/nAk92o8yngL0lbSlJwETgUeAu4Ii0z2Tglm6UYWZm3VRKgvgUG4b7vgkYRnZbapdExBzgBrJbWR9OMcwAvgScLmkRMBSY2dUyzMys+zpsYoqI58luSy2biDgPOK9o9RPAnuUsx8zMus5jMZmZWS4nCDMzy9VhE5OkfSPiTx2tM+st/NS0WaaUGsT3S1xnZma9SJs1CEn7kD2fMFzS6QWbtgH6VTowMzOrrfaamDYDtk77DCxY/wIbnlcwM7Neqr3B+u4B7pF0RUQsqWJMZmZWB0oZamNzSTOAhsL9I+KASgVlZma1V0qC+DlwGfBjYF1lwzErncdZMqusUhJES0R4XoY+qje+Cfs21vrm2frqRym3ud4q6SRJ20sa0vpV8cjMzKymSqlBTE7fv1iwLoCdyh+OmZnVi1IG6xtTjUDMzKy+lDLUxify1kfET8sfjpmZ1YtSmpjeXvB6ANkEP/MBJwgzs16slCamUwuXJQ0CrqpYRGZmBXrjnXQ9RVeG+34R2KXcgZiZWX0ppQ/iVrK7liAbpO/NwKzuFCppMNmDd+PSuT8F/A24nuyJ7cXAUWk2OzMzq4FS+iAuLHjdAiyJiKZulvs94NcRcYSkzYAtgbOBOyNiuqRpwDSyearN6p4fvrPeqMMmpjRo31/JRnR9HfBqdwqUtA3wbmBmOv+rEbESmARcmXa7Eji8O+WYmVn3dJggJB0FPAAcCRwFzJHUneG+dwKagZ9IelDSjyVtBWwXEc8CpO/bdqMMMzPrplKamM4B3h4RywAkDQd+B9zQjTInAKdGxBxJ3yNrTiqJpKnAVIDRo0d3MQQzM+tIKQlik9bkkKyga3c/tWoCmiJiTlq+gSxBPCdp+4h4VtL2wLK8gyNiBjADoLGxMfL2sb7LfQFm5VPKG/2vJf1G0hRJU4DbgV91tcCI+CfwtKRd06qJwKPAbDaM+zQZuKWrZZiZWfeV8qDcFyV9GHgnIGBGRNzczXJPBa5OdzA9AXySLFnNknQ88BRZn4eZmdVImwlC0hvIOo7/FBE3ATel9e+WtHNE/KOrhUbEAqAxZ9PErp7TzMzKq70mpu8Cq3PWv5i2mZlZL9ZegmiIiL8Ur4yIuWRPO5uZWS/WXh/EgHa2bVHuQMzMusrTlFZGezWIP0s6oXhl6kSeV7mQzMysHrRXg/g8cLOkY9mQEBqBzYAPVTowMzOrrTYTREQ8B7xD0nvIRl0FuD0ifl+VyMzMrKZKeQ7iLuCuKsRiZmZ1pDtDZpiZWS/mBGFmZrmcIMzMLFcpo7ma1YTvbTerLdcgzMwslxOEmZnlcoIwM7NcThBmZpbLndTWY3g6UbPqcg3CzMxyOUGYmVmumiUISf0kPSjptrQ8RtIcSX+XdH2ar9rMzGqkln0QnwMeA7ZJy98AvhMR10m6DDge+EGtgjOz+uX+qOqoSQ1C0ijgEODHaVnAAcANaZcrgcNrEZuZmWVq1cT0XeBM4D9peSiwMiJa0nITMDLvQElTJc2VNLe5ubnykZqZ9VFVb2KSdCiwLCLmSdq/dXXOrpF3fETMAGYANDY25u5j9cfjKpn1PLXog9gXOEzSB4ABZH0Q3wUGS9o01SJGAUtrEJuZmSVVb2KKiLMiYlRENABHA7+PiGPJZq07Iu02Gbil2rGZmdkG9fQcxJeA0yUtIuuTmFnjeMzM+rSaDrUREXcDd6fXTwB71jIeMzPboJ5qEGZmVkecIMzMLJdHczWzXifvSWvfWt15ThBWFzx0gln9cYKwmnBCMKt/fTZBuApqZtY+d1KbmVkuJwgzM8vVZ5uYrHw8EJ9Z7+QahJmZ5XKCMDOzXG5isrLzLaxmvYNrEGZmlssJwszMcjlBmJlZLicIMzPL5U5qM+sT/LxO51W9BiFpB0l3SXpM0iOSPpfWD5F0h6S/p++vq3ZsZma2QS1qEC3AGRExX9JAYJ6kO4ApwJ0RMV3SNGAa2TzVVmd8G6tZ31D1GkREPBsR89Pr1cBjwEhgEnBl2u1K4PBqx2ZmZhvUtJNaUgPwVmAOsF1EPAtZEgG2beOYqZLmSprb3NxcrVDNzPqcmnVSS9oauBH4fES8IKmk4yJiBjADoLGxMSoXYX3zfBZmVmk1SRCS+pMlh6sj4qa0+jlJ20fEs5K2B5bVIra+xnd2mFlbanEXk4CZwGMR8e2CTbOByen1ZOCWasdmZmYb1KIGsS9wHPCwpAVp3dnAdGCWpOOBp4AjaxBbr1bK3Ue+Q8n6CteeO1b1BBERfwTa6nCYWM1YzMysbR5qw8zMcjlBmJlZLo/FZGbWhr7eT+EE0Yv09T9mMysvNzGZmVkuJwgzM8vlBGFmZrncB1EHKjWukh96M+sdajX2mmsQZmaWywnCzMxyOUGYmVku90GYmeE+uzxOED2E/3jNrNqcIOqUE4KZ1Zr7IMzMLJcThJmZ5XITU5nV6oEWM7Nyq7sEIekg4HtAP+DHETG9VrGUa3RUj7Jq1jv0tf/lukoQkvoBlwAHAk3AnyXNjohHq1G+O4bNrDPK9Z5Rr4mm3vog9gQWRcQTEfEqcB0wqcYxmZn1SYqIWsewnqQjgIMi4tNp+Thgr4g4pWCfqcDUtLgr8LcuFjcMWN6NcCulXuOC+o3NcXWO4+qc3hjXjhExvKOd6qqJCVDOuo0yWETMAGZ0uyBpbkQ0dvc85VavcUH9xua4OsdxdU5fjqvempiagB0KlkcBS2sUi5lZn1ZvCeLPwC6SxkjaDDgamF3jmMzM+qS6amKKiBZJpwC/IbvN9fKIeKRCxXW7mapC6jUuqN/YHFfnOK7O6bNx1VUntZmZ1Y96a2IyM7M64QRhZma5en2CkHS5pGWSFraxXZIukrRI0l8kTaiTuPaXtErSgvT1lSrEtIOkuyQ9JukRSZ/L2afq16vEuGpxvQZIekDSQymur+bss7mk69P1miOpoU7imiKpueB6fbrScRWU3U/Sg5Juy9lW9etVYly1vF6LJT2cyp2bs71y/5MR0au/gHcDE4CFbWz/APArsmcw9gbm1Elc+wO3VflabQ9MSK8HAo8DY2t9vUqMqxbXS8DW6XV/YA6wd9E+JwGXpddHA9fXSVxTgIureb0Kyj4duCbv91WL61ViXLW8XouBYe1sr9j/ZK+vQUTEvcC/2tllEvDTyNwPDJa0fR3EVXUR8WxEzE+vVwOPASOLdqv69SoxrqpL12BNWuyfvorv+pgEXJle3wBMlJT3QGi146oJSaOAQ4Aft7FL1a9XiXHVs4r9T/b6BFGCkcDTBctN1MGbT7JPaib4laTdqllwqtq/lezTZ6GaXq924oIaXK/ULLEAWAbcERFtXq+IaAFWAUPrIC6Aj6QmiRsk7ZCzvRK+C5wJ/KeN7TW5XiXEBbW5XpAl999KmqdsqKFiFfufdIIoYXiPGplPNl7KHsD3gV9Uq2BJWwM3Ap+PiBeKN+ccUpXr1UFcNbleEbEuIsaTPfW/p6RxRbvU5HqVENetQENEvAX4HRs+tVeMpEOBZRExr73dctZV9HqVGFfVr1eBfSNiAnAwcLKkdxdtr9g1c4Ko0+E9IuKF1maCiPgl0F/SsEqXK6k/2Zvw1RFxU84uNbleHcVVq+tVUP5K4G7goKJN66+XpE2BQVSxabGtuCJiRUS8khZ/BLytCuHsCxwmaTHZSM0HSPpZ0T61uF4dxlWj69Va9tL0fRlwM9mo14X11HDqAAADWUlEQVQq9j/pBJEN5fGJdCfA3sCqiHi21kFJen1r26ukPcl+VysqXKaAmcBjEfHtNnar+vUqJa4aXa/hkgan11sA7wX+WrTbbGByen0E8PtIPYu1jKuojfowsn6dioqIsyJiVEQ0kHVA/z4iPl60W9WvVylx1eJ6pXK3kjSw9TXwPqD4zseK/U/W1VAblSDpWrI7XIZJagLOI+u0IyIuA35JdhfAIuBF4JN1EtcRwGcltQAvAUdX+h+F7JPUccDDqf0a4GxgdEFctbhepcRVi+u1PXClsomuNgFmRcRtkv4bmBsRs8kS21WSFpF9Ej66wjGVGtdpkg4DWlJcU6oQV646uF6lxFWr67UdcHP67LMpcE1E/FrSiVD5/0kPtWFmZrncxGRmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCrIikdWnkzIWSbm19pqCd/QdLOqlgeYSkGyofqVll+TZXsyKS1kTE1un1lcDjEXFBO/s3kI0AWjychVmP5hqEWfvuIw18JmlrSXdKmq9sfP5JaZ/pwM6p1vEtSQ1K83wom0fgJkm/lvR3Sd9sPbGk4yU9LuluST+SdHFaf2SqvTwk6d4q/7xm6/X6J6nNuio9iTyR7OlegJeBD0XEC2mcp/slzQamAePS4HitNYpC48lGoH0F+Juk7wPrgC+TzQmyGvg98FDa/yvA+yPimY6at8wqyTUIs9faIg3psQIYAtyR1gv4uqS/kI3oOZJsKISO3BkRqyLiZeBRYEeyAdfuiYh/RcRa4OcF+/8JuELSCUC/svxEZl3gBGH2Wi+l2sCOwGbAyWn9scBw4G1p+3PAgBLO90rB63VkNfc2J8GJiBOBc8lG6FwgqRrzIZi9hhOEWRsiYhVwGvCFNNz4ILJ5A9ZKeg9ZAoGsiWhgJ0//ALCfpNelYa0/0rpB0s4RMScivgIsZ+OhnM2qxn0QZu2IiAclPUQ2qujVwK3KJo5fQBpCOyJWSPpT6pj+FXBJCed9RtLXyWbGW0rW9LQqbf6WpF3Iahl3sqFvwqyqfJurWY1I2joi1qQaxM3A5RFxc63jMmvlJiaz2jk/dYYvBJ6kitPKmpXCNQgzM8vlGoSZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZrv8DivjXyQtE3eYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie_stats.plot.hist(bins=70)\n",
    "plt.title(\"Distribution of ratings per User\")\n",
    "plt.ylabel('Count of Movies')\n",
    "plt.xlabel('Ratings');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions look similar to that with UserID but here we have more pronounced peaks at ratings 1,2,5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I have increased the minimum movie rating count from 100 to 1000 in order to reduce computational time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from length: 1000209\n",
      "to length: 305373\n"
     ]
    }
   ],
   "source": [
    "minMovieRatings = 1000\n",
    "filterMovies = lens['MovieID'].value_counts() > minMovieRatings\n",
    "filterMovies = filterMovies[filterMovies].index\n",
    "filterMovies = lens[lens['MovieID'].isin(filterMovies)]\n",
    "print('from length:', len(lens))\n",
    "print('to length:', len(filterMovies))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Prediction Algorithms\n",
    "\n",
    "Surprise provides a bunch of built-in algorithms. Here, `SVD()`, `SlopeOne()`, `NMF()` and `KNNBasic()` have been used. The code for looping through the respective algorithms is sourced from\n",
    "https://towardsdatascience.com/building-and-testing-recommender-systems-with-surprise-step-by-step-d4ba702ef80b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.8677  0.8700  0.8700  0.8692  0.0011  \n",
      "MAE (testset)     0.6794  0.6805  0.6804  0.6801  0.0005  \n",
      "Fit time          20.81   20.57   20.18   20.52   0.26    \n",
      "Test time         1.44    1.32    1.80    1.52    0.21    \n",
      "Evaluating RMSE, MAE of algorithm SlopeOne on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.8857  0.8857  0.8875  0.8863  0.0009  \n",
      "MAE (testset)     0.6916  0.6923  0.6926  0.6922  0.0004  \n",
      "Fit time          0.78    0.81    0.81    0.80    0.01    \n",
      "Test time         9.29    9.11    9.31    9.24    0.09    \n",
      "Evaluating RMSE, MAE of algorithm NMF on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.9006  0.8973  0.9040  0.9006  0.0027  \n",
      "MAE (testset)     0.7094  0.7034  0.7091  0.7073  0.0028  \n",
      "Fit time          20.57   23.68   24.81   23.02   1.79    \n",
      "Test time         1.28    1.41    1.55    1.41    0.11    \n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Evaluating RMSE, MAE of algorithm KNNBasic on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.9034  0.9141  0.9122  0.9099  0.0047  \n",
      "MAE (testset)     0.7077  0.7148  0.7122  0.7116  0.0029  \n",
      "Fit time          27.42   25.69   26.00   26.37   0.76    \n",
      "Test time         168.90  167.97  167.64  168.17  0.53    \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>test_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVD</th>\n",
       "      <td>0.869231</td>\n",
       "      <td>0.680127</td>\n",
       "      <td>20.519792</td>\n",
       "      <td>1.518942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SlopeOne</th>\n",
       "      <td>0.886308</td>\n",
       "      <td>0.692162</td>\n",
       "      <td>0.802524</td>\n",
       "      <td>9.235631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NMF</th>\n",
       "      <td>0.900623</td>\n",
       "      <td>0.707270</td>\n",
       "      <td>23.020115</td>\n",
       "      <td>1.414833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNNBasic</th>\n",
       "      <td>0.909910</td>\n",
       "      <td>0.711571</td>\n",
       "      <td>26.369369</td>\n",
       "      <td>168.170674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           test_rmse  test_mae   fit_time   test_time\n",
       "Algorithm                                            \n",
       "SVD         0.869231  0.680127  20.519792    1.518942\n",
       "SlopeOne    0.886308  0.692162   0.802524    9.235631\n",
       "NMF         0.900623  0.707270  23.020115    1.414833\n",
       "KNNBasic    0.909910  0.711571  26.369369  168.170674"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from surprise import SVD\n",
    "from surprise import SlopeOne\n",
    "from surprise import NMF\n",
    "from surprise import KNNBasic\n",
    "from surprise import Dataset\n",
    "from surprise import Reader\n",
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "# Creation of the dataframe. Column names are irrelevant.\n",
    "ratings_dict = {'userID': filterMovies['UserID'],\n",
    "                'itemID': filterMovies['MovieID'],\n",
    "                'rating': filterMovies['Rating']}\n",
    "df = pd.DataFrame(ratings_dict)\n",
    "\n",
    "# A reader is still needed but only the rating_scale param is required.\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "\n",
    "# The columns must correspond to user id, item id and ratings (in that order).\n",
    "data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']], reader)\n",
    "\n",
    "# We can now use this dataset as we please, e.g. calling cross_validate\n",
    "results_table = []\n",
    "for algorithm in [SVD(), SlopeOne(), NMF(), KNNBasic()]:\n",
    "    #running 3 fold cross validation\n",
    "    results = cross_validate(algorithm, data, measures = ['RMSE', 'MAE'], cv = 3, verbose = True)\n",
    "    \n",
    "    #pushing algo names into results\n",
    "    data_final = pd.DataFrame.from_dict(results).mean(axis = 0)\n",
    "    data_final = data_final.append(pd.Series([str(algorithm).split(' ')[0].split('.')[-1]], index = ['Algorithm']))\n",
    "    results_table.append(data_final)\n",
    "pd.DataFrame(results_table).set_index('Algorithm').sort_values('test_rmse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `rmse` as our accuracy metric. Here, we can see that, SlopeOne algorithm strikes the best balance between lower `rmse` value and lower fit and test time in comparision to other algorithms. In particular KNNBasic took the highest time to process. SVD++ was initially my choice instead of SVD but given the size of the dataset and looking at the processing time it took in some cases which I found on the internet(over 2 hours), I chose not to include it."
   ]
  }
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