Clustering_Recommendation1 (1).ipynb

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    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import turicreate\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.decomposition import PCA"
   ]
  },
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    "#reading order_products_train.csv file which contains Data curated on the products added to cart and their order\n",
    "order_products_train_df = pd.read_csv(\"order_products__train.csv\")\n",
    "order_products_prior_df = pd.read_csv(\"order_products__prior.csv\")\n",
    "\n",
    "# reading orders.csv which provides information on when and after how long an order was placed by user\n",
    "orders_df = pd.read_csv(\"orders.csv\")\n",
    "\n",
    "#reading order_products_train.csv file which categorises each data based on department and Aisle\n",
    "products_df = pd.read_csv(\"products.csv\")\n",
    "\n",
    "# reading aisles.csv that provides information on product names across the Aisles\n",
    "aisles_df = pd.read_csv(\"aisles.csv\")\n",
    "\n",
    "#reading order_products_train.csv file which provides information for 21 different departments\n",
    "departments_df = pd.read_csv(\"departments.csv\")"
   ]
  },
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     "text": [
      "1384617\n",
      "32434489\n",
      "3421083\n",
      "49688\n",
      "134\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of rows in each dataframe \n",
    "print(len(order_products_train_df))\n",
    "print(len(order_products_prior_df))\n",
    "print(len(orders_df))\n",
    "print(len(products_df))\n",
    "print(len(aisles_df))\n",
    "print(len(departments_df))"
   ]
  },
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     "execution_count": 8,
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   "source": [
    "# Getting the number of unique users\n",
    "len(orders_df.user_id.unique())"
   ]
  },
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       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         1       49302                  1          1\n",
       "1         1       11109                  2          1\n",
       "2         1       10246                  3          0\n",
       "3         1       49683                  4          0\n",
       "4         1       43633                  5          1"
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    "order_products_train_df.head()"
   ]
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       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         2       33120                  1          1\n",
       "1         2       28985                  2          1\n",
       "2         2        9327                  3          0\n",
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    "order_products_prior_df.head()"
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       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "5   3367565        1    prior             6          2                  7   \n",
       "6    550135        1    prior             7          1                  9   \n",
       "7   3108588        1    prior             8          1                 14   \n",
       "8   2295261        1    prior             9          1                 16   \n",
       "9   2550362        1    prior            10          4                  8   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  \n",
       "5                    19.0  \n",
       "6                    20.0  \n",
       "7                    14.0  \n",
       "8                     0.0  \n",
       "9                    30.0  "
      ]
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       "      <td>Chocolate Sandwich Cookies</td>\n",
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       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  "
      ]
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      "text/plain": [
       "   aisle_id                       aisle\n",
       "0         1       prepared soups salads\n",
       "1         2           specialty cheeses\n",
       "2         3         energy granola bars\n",
       "3         4               instant foods\n",
       "4         5  marinades meat preparation"
      ]
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department_id department\n",
       "0              1     frozen\n",
       "1              2      other\n",
       "2              3     bakery\n",
       "3              4    produce\n",
       "4              5    alcohol"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gDoYhx_dmxQ3",
    "outputId": "aef50afa-b220-41ec-8ebe-f175774e5842"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3421083"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders_df.order_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OaOHYRdKmxQ3",
    "outputId": "f7f55bdf-733c-4093-8050-856584e9674a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eval_set\n",
       "prior    206209\n",
       "test      75000\n",
       "train    131209\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the unique rows in each eval set\n",
    "orders_df.groupby(\"eval_set\")['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hMtU21pbmxQ3",
    "outputId": "85e975c6-9849-400c-b36a-bce7162b15a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_dow'>"
      ]
     },
     "execution_count": 31,
     "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": [
    "# Checking the order trend in each day of the week\n",
    "orders_df.groupby('order_dow')['order_id'].nunique().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OOziHD56mxQ3",
    "outputId": "032de083-09dc-4ef8-c782-00968baf8c0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_hour_of_day'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "# Barplot to check trend of orders in each day of the week\n",
    "orders_df.groupby('order_hour_of_day')['order_id'].nunique().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fNMebyscmxQ3",
    "outputId": "392b7f77-b54e-4aaa-997f-52df97b861fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='days_since_prior_order'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "# Trend in days since prior order and frequncy\n",
    "orders_df.groupby('days_since_prior_order')['order_id'].nunique().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5J-lFPSXmxQ3",
    "outputId": "2bd77bf3-8aa4-4e56-c041-51662755ea28"
   },
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Michigan Organic Kale</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Garlic Powder</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "      <td>spices seasonings</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Coconut Butter</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>oils vinegars</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Natural Sweetener</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>baking ingredients</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered           product_name  \\\n",
       "0         2       33120                  1          1     Organic Egg Whites   \n",
       "1         2       28985                  2          1  Michigan Organic Kale   \n",
       "2         2        9327                  3          0          Garlic Powder   \n",
       "3         2       45918                  4          1         Coconut Butter   \n",
       "4         2       30035                  5          0      Natural Sweetener   \n",
       "\n",
       "   aisle_id  department_id               aisle  department  \n",
       "0        86             16                eggs  dairy eggs  \n",
       "1        83              4    fresh vegetables     produce  \n",
       "2       104             13   spices seasonings      pantry  \n",
       "3        19             13       oils vinegars      pantry  \n",
       "4        17             13  baking ingredients      pantry  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging orders and order_products_prior_df dataframes on products_df, aisles_df, departments_df\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, products_df, on='product_id', how='left')\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, aisles_df, on='aisle_id', how='left')\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, departments_df, on='department_id', how='left')\n",
    "order_products_prior_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RuYcE4ALmxQ4",
    "outputId": "4e2ab2f1-3fab-464e-cce6-163e51ca2237"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Grouping to check the highest selling products \n",
    "order_products_prior_df['product_name'].value_counts().head(30).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ITYOFjBZmxQ4",
    "outputId": "ac5f2a31-39a1-4d54-af1e-9b2ef572c1fb"
   },
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered  user_id eval_set  \\\n",
       "0         2       33120                  1          1   202279    prior   \n",
       "1         2       28985                  2          1   202279    prior   \n",
       "2         2        9327                  3          0   202279    prior   \n",
       "3         2       45918                  4          1   202279    prior   \n",
       "4         2       30035                  5          0   202279    prior   \n",
       "\n",
       "   order_number  order_dow  order_hour_of_day  days_since_prior_order  \n",
       "0             3          5                  9                     8.0  \n",
       "1             3          5                  9                     8.0  \n",
       "2             3          5                  9                     8.0  \n",
       "3             3          5                  9                     8.0  \n",
       "4             3          5                  9                     8.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A9fa3vlmmxQ4"
   },
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "brHAXYHsmxQ5",
    "outputId": "48735323-b3df-4e03-d7b0-e5962da68944"
   },
   "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",
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       "    }\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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic 4% Milk Fat Whole Milk Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>other creams cheeses</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Organic Celery Hearts</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Cucumber Kirby</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Lightly Smoked Sardines in Olive Oil</td>\n",
       "      <td>95</td>\n",
       "      <td>15</td>\n",
       "      <td>canned meat seafood</td>\n",
       "      <td>canned goods</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered  \\\n",
       "0         1       49302                  1          1   \n",
       "1         1       11109                  2          1   \n",
       "2         1       10246                  3          0   \n",
       "3         1       49683                  4          0   \n",
       "4         1       43633                  5          1   \n",
       "\n",
       "                                    product_name  aisle_id  department_id  \\\n",
       "0                               Bulgarian Yogurt       120             16   \n",
       "1  Organic 4% Milk Fat Whole Milk Cottage Cheese       108             16   \n",
       "2                          Organic Celery Hearts        83              4   \n",
       "3                                 Cucumber Kirby        83              4   \n",
       "4           Lightly Smoked Sardines in Olive Oil        95             15   \n",
       "\n",
       "                  aisle    department  \n",
       "0                yogurt    dairy eggs  \n",
       "1  other creams cheeses    dairy eggs  \n",
       "2      fresh vegetables       produce  \n",
       "3      fresh vegetables       produce  \n",
       "4   canned meat seafood  canned goods  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concating dataframes\n",
    "data_merged = pd.concat([order_products_train_df, order_products_prior_df])\n",
    "data_merged = pd.merge(data_merged, products_df, on='product_id', how='left')\n",
    "data_merged = pd.merge(data_merged, aisles_df, on='aisle_id', how='left')\n",
    "data_merged = pd.merge(data_merged, departments_df, on='department_id', how='left')\n",
    "data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LRKQpLWJmxQ5",
    "outputId": "f5186f2b-7898-4fdd-cb9e-d3cf6e1e5f4e"
   },
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic 4% Milk Fat Whole Milk Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>other creams cheeses</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Organic Celery Hearts</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "      <td>112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Cucumber Kirby</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "      <td>112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Lightly Smoked Sardines in Olive Oil</td>\n",
       "      <td>95</td>\n",
       "      <td>15</td>\n",
       "      <td>canned meat seafood</td>\n",
       "      <td>canned goods</td>\n",
       "      <td>112108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered  \\\n",
       "0         1       49302                  1          1   \n",
       "1         1       11109                  2          1   \n",
       "2         1       10246                  3          0   \n",
       "3         1       49683                  4          0   \n",
       "4         1       43633                  5          1   \n",
       "\n",
       "                                    product_name  aisle_id  department_id  \\\n",
       "0                               Bulgarian Yogurt       120             16   \n",
       "1  Organic 4% Milk Fat Whole Milk Cottage Cheese       108             16   \n",
       "2                          Organic Celery Hearts        83              4   \n",
       "3                                 Cucumber Kirby        83              4   \n",
       "4           Lightly Smoked Sardines in Olive Oil        95             15   \n",
       "\n",
       "                  aisle    department  user_id  \n",
       "0                yogurt    dairy eggs   112108  \n",
       "1  other creams cheeses    dairy eggs   112108  \n",
       "2      fresh vegetables       produce   112108  \n",
       "3      fresh vegetables       produce   112108  \n",
       "4   canned meat seafood  canned goods   112108  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging merged dataframe with orders_df \n",
    "data_merged = pd.merge(data_merged, orders_df[['order_id','user_id']], on='order_id', how='left')\n",
    "data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "59ADIE0LmxQ5"
   },
   "outputs": [],
   "source": [
    "# Getting the data which come under top 80% of the ordered departments only\n",
    "top80_orders = data_merged['department'].value_counts(normalize=True)\n",
    "cols_top80 = list(top80_orders[(top80_orders * 100).cumsum() < 80].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sjwFW3JBmxQ5"
   },
   "outputs": [],
   "source": [
    "# Making a sparse matrix representing users as rows and deartments as columns\n",
    "cluster_data = data_merged.reset_index().pivot_table(index='user_id', columns='department', values = 'order_id', aggfunc='count')[cols_top80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1NScqDHymxQ5"
   },
   "outputs": [],
   "source": [
    "cluster_data.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b3UJlKBEmxQ6",
    "outputId": "4289f907-8cfa-4797-c8ab-edb8d82ab910"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sum_of_squared_distances = []\n",
    "silhouette = []\n",
    "\n",
    "# Applying KMeans clustering for each K value in 2-10\n",
    "K = range(2,10)\n",
    "for num_clusters in K :\n",
    "  print(num_clusters)\n",
    "  kmeans = KMeans(n_clusters=num_clusters)\n",
    "  kmeans.fit(cluster_data)\n",
    "  #Sum_of_squared_distances.append(kmeans.inertia_)\n",
    "  silhouette.append(silhouette_score(cluster_data, kmeans.labels_, metric='euclidean'))\n",
    "\n",
    "plt.plot(K, silhouette,'bx-')\n",
    "plt.xlabel('Values of K') \n",
    "plt.ylabel('Sum of squared distances/Inertia') \n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MsytYUssmxQ6",
    "outputId": "091c5898-4d04-4815-a0d8-01e2cab789a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting KMeans to most optimal k value\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(cluster_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hfrPPHvemxQ6",
    "outputId": "78149e81-b704-4cca-8667-c1209e0a1ffd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>department</th>\n",
       "      <th>produce</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>snacks</th>\n",
       "      <th>beverages</th>\n",
       "      <th>frozen</th>\n",
       "      <th>pantry</th>\n",
       "      <th>bakery</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department  produce  dairy eggs  snacks  beverages  frozen  pantry  bakery\n",
       "user_id                                                                   \n",
       "1               5.0        17.0    25.0       15.0     0.0     1.0     0.0\n",
       "2              43.0        49.0    47.0        9.0    28.0    11.0     2.0\n",
       "3              38.0        21.0     9.0        3.0     6.0     4.0     0.0\n",
       "4               2.0         0.0     1.0        3.0     3.0     0.0     2.0\n",
       "5              23.0         9.0     1.0        0.0     2.0     3.0     0.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cD0R1qaPmxQ6"
   },
   "outputs": [],
   "source": [
    "# Getting the labels out of the clusters to interpret them\n",
    "cluster_data['labels'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TsO0M70SmxQ6"
   },
   "outputs": [],
   "source": [
    "# Next applying PCA as data is in higher dimention \n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(cluster_data)\n",
    "# pca_samples = pca.transform(cluster_data)\n",
    "pca_samples = pd.DataFrame(pca.fit_transform(cluster_data),columns=['PC1','PC2']) \n",
    "pca_samples['cluster'] = pd.Categorical(kmeans.labels_)\n",
    "\n",
    "centers = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B6rNi8jmmxQ6",
    "outputId": "005a301f-042f-4d26-efb3-c467e91f2964"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>department</th>\n",
       "      <th>produce</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>snacks</th>\n",
       "      <th>beverages</th>\n",
       "      <th>frozen</th>\n",
       "      <th>pantry</th>\n",
       "      <th>bakery</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206205</th>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>34.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>197.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206209</th>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "department  produce  dairy eggs  snacks  beverages  frozen  pantry  bakery\n",
       "user_id                                                                   \n",
       "1               5.0        17.0    25.0       15.0     0.0     1.0     0.0\n",
       "2              43.0        49.0    47.0        9.0    28.0    11.0     2.0\n",
       "3              38.0        21.0     9.0        3.0     6.0     4.0     0.0\n",
       "4               2.0         0.0     1.0        3.0     3.0     0.0     2.0\n",
       "5              23.0         9.0     1.0        0.0     2.0     3.0     0.0\n",
       "...             ...         ...     ...        ...     ...     ...     ...\n",
       "206205         14.0        20.0     0.0        1.0     3.0     1.0     2.0\n",
       "206206         34.0        39.0    42.0       33.0    76.0    14.0     2.0\n",
       "206207         50.0        52.0    23.0       20.0    21.0    15.0     3.0\n",
       "206208        197.0       161.0    59.0       20.0    26.0    38.0    62.0\n",
       "206209         20.0        29.0    12.0       11.0     3.0     8.0    10.0\n",
       "\n",
       "[206209 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1f-wjk-EmxQ6",
    "outputId": "64fc827f-67bc-4520-942a-c9fdb1a06f13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PC1', ylabel='PC2'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Plotting clusters over principal component 1 and 2\n",
    "sns.scatterplot(x=\"PC1\",y=\"PC2\",hue=\"cluster\",data=pca_samples,\n",
    "               palette=['green','orange','dodgerblue','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oic0F6j0mxQ6",
    "outputId": "d535cfa9-4182-4368-ced0-aff87639893e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# Scatter plot for thr pca values \n",
    "scatter = ax.scatter(pca_samples[:,0], pca_samples[:,1], c=kmeans.labels_, s=50)\n",
    "\n",
    "plt.xlabel('x_values')\n",
    "plt.ylabel('y_values')\n",
    "plt.legend(kmeans.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gxoX0nQLmxQ7",
    "outputId": "aab7662f-4580-4783-e660-aad32c3b21f0"
   },
   "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>department</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department  user_id\n",
       "labels             \n",
       "0             42787\n",
       "1            144776\n",
       "2             14677\n",
       "3              3969"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.reset_index()[['labels','user_id']].groupby('labels').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1rcOhH8KmxQ7",
    "outputId": "21a539e1-b08b-4028-a614-3ffe3ff98036"
   },
   "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>department</th>\n",
       "      <th>produce</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>snacks</th>\n",
       "      <th>beverages</th>\n",
       "      <th>frozen</th>\n",
       "      <th>pantry</th>\n",
       "      <th>bakery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3243825.0</td>\n",
       "      <td>1855679.0</td>\n",
       "      <td>983735.0</td>\n",
       "      <td>925598.0</td>\n",
       "      <td>793061.0</td>\n",
       "      <td>644167.0</td>\n",
       "      <td>400551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2417849.0</td>\n",
       "      <td>1581155.0</td>\n",
       "      <td>981239.0</td>\n",
       "      <td>1049672.0</td>\n",
       "      <td>825416.0</td>\n",
       "      <td>656422.0</td>\n",
       "      <td>384419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2684175.0</td>\n",
       "      <td>1480991.0</td>\n",
       "      <td>721088.0</td>\n",
       "      <td>599388.0</td>\n",
       "      <td>506226.0</td>\n",
       "      <td>448107.0</td>\n",
       "      <td>299252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1542529.0</td>\n",
       "      <td>713242.0</td>\n",
       "      <td>320350.0</td>\n",
       "      <td>229517.0</td>\n",
       "      <td>212155.0</td>\n",
       "      <td>208123.0</td>\n",
       "      <td>140959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "department    produce  dairy eggs    snacks  beverages    frozen    pantry  \\\n",
       "0           3243825.0   1855679.0  983735.0   925598.0  793061.0  644167.0   \n",
       "1           2417849.0   1581155.0  981239.0  1049672.0  825416.0  656422.0   \n",
       "2           2684175.0   1480991.0  721088.0   599388.0  506226.0  448107.0   \n",
       "3           1542529.0    713242.0  320350.0   229517.0  212155.0  208123.0   \n",
       "\n",
       "department    bakery  \n",
       "0           400551.0  \n",
       "1           384419.0  \n",
       "2           299252.0  \n",
       "3           140959.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the frequency count of each cluster for each department (top 8)\n",
    "cluster_data.groupby('labels').sum().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dVSc0sijmxQ7"
   },
   "outputs": [],
   "source": [
    "# Saving the data to csv\n",
    "cluster_data.groupby('labels').sum().to_csv('clustering_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DIfVhFlXmxQ7"
   },
   "outputs": [],
   "source": [
    "# Setting up new columns based on day of week\n",
    "orders_df.loc[orders_df['order_dow'].isin([0,1]), 'Weekday_Tag'] = 'Weekend'\n",
    "orders_df.loc[orders_df['order_dow'].isin([2,3,4,5,6]), 'Weekday_Tag'] = 'Weekday'\n",
    "\n",
    "# Adding a new column, Hour_tag containing categorical values based on integer values\n",
    "orders_df.loc[orders_df['order_hour_of_day'].isin([5,6,7,8,9,10,11]), 'Hour_Tag'] = 'Morning'\n",
    "orders_df.loc[orders_df['order_hour_of_day'].isin([12,13,14,15,16,17]), 'Hour_Tag'] = 'Lunch'\n",
    "orders_df.loc[orders_df['order_hour_of_day'].isin([18,19,20,21,22,23]), 'Hour_Tag'] = 'Dinner'\n",
    "orders_df.loc[orders_df['order_hour_of_day'].isin([24,0,1,2,3,4]), 'Hour_Tag'] = 'Late_Night'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fLxD9YGsmxQ7"
   },
   "outputs": [],
   "source": [
    "orders_df['Concat_Tag'] =  orders_df[['Weekday_Tag', 'Hour_Tag']].agg('_'.join, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XMmq2PUYmxQ7"
   },
   "outputs": [],
   "source": [
    "# Making another cluster based on the new encoded dataframe\n",
    "cluster_2 = orders_df.reset_index().pivot_table(index='user_id', columns=['Concat_Tag'], \n",
    "                                                values = 'order_id', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sIjNxQmJmxQ7"
   },
   "outputs": [],
   "source": [
    "cluster_2.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IczOvPjEmxQ7",
    "outputId": "00d76091-af30-4a03-efc2-998f48975ddc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sum_of_squared_distances = []\n",
    "silhouette = []\n",
    "\n",
    "# Again using KMeans to get optimal K value\n",
    "K = range(2,10)\n",
    "for num_clusters in K :\n",
    "  print(num_clusters)\n",
    "  kmeans = KMeans(n_clusters=num_clusters)\n",
    "  kmeans.fit(cluster_2)\n",
    "  #Sum_of_squared_distances.append(kmeans.inertia_)\n",
    "  silhouette.append(silhouette_score(cluster_2, kmeans.labels_, metric='euclidean'))\n",
    "\n",
    "plt.plot(K, silhouette,'bx-')\n",
    "plt.xlabel('Values of K') \n",
    "plt.ylabel('Sum of squared distances/Inertia') \n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1K9duPq0mxQ7",
    "outputId": "f2e5b85a-1ede-4a06-fd4b-28e13ba40315"
   },
   "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>Concat_Tag</th>\n",
       "      <th>Weekday_Dinner</th>\n",
       "      <th>Weekday_Late_Night</th>\n",
       "      <th>Weekday_Lunch</th>\n",
       "      <th>Weekday_Morning</th>\n",
       "      <th>Weekend_Dinner</th>\n",
       "      <th>Weekend_Late_Night</th>\n",
       "      <th>Weekend_Lunch</th>\n",
       "      <th>Weekend_Morning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32514.0</td>\n",
       "      <td>2991.0</td>\n",
       "      <td>93510.0</td>\n",
       "      <td>159382.0</td>\n",
       "      <td>17493.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>52730.0</td>\n",
       "      <td>93101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132796.0</td>\n",
       "      <td>14021.0</td>\n",
       "      <td>269510.0</td>\n",
       "      <td>202127.0</td>\n",
       "      <td>70284.0</td>\n",
       "      <td>6942.0</td>\n",
       "      <td>179632.0</td>\n",
       "      <td>120485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27146.0</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>116722.0</td>\n",
       "      <td>49944.0</td>\n",
       "      <td>11092.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>43955.0</td>\n",
       "      <td>21957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16832.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>60757.0</td>\n",
       "      <td>104753.0</td>\n",
       "      <td>8198.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>23192.0</td>\n",
       "      <td>37939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86467.0</td>\n",
       "      <td>6158.0</td>\n",
       "      <td>208477.0</td>\n",
       "      <td>93711.0</td>\n",
       "      <td>36203.0</td>\n",
       "      <td>2857.0</td>\n",
       "      <td>104924.0</td>\n",
       "      <td>50939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>114621.0</td>\n",
       "      <td>9522.0</td>\n",
       "      <td>298352.0</td>\n",
       "      <td>129068.0</td>\n",
       "      <td>53747.0</td>\n",
       "      <td>4885.0</td>\n",
       "      <td>167108.0</td>\n",
       "      <td>77719.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Concat_Tag  Weekday_Dinner  Weekday_Late_Night  Weekday_Lunch  \\\n",
       "labels                                                          \n",
       "0                  32514.0              2991.0        93510.0   \n",
       "1                 132796.0             14021.0       269510.0   \n",
       "2                  27146.0              1944.0       116722.0   \n",
       "3                  16832.0              1375.0        60757.0   \n",
       "4                  86467.0              6158.0       208477.0   \n",
       "5                 114621.0              9522.0       298352.0   \n",
       "\n",
       "Concat_Tag  Weekday_Morning  Weekend_Dinner  Weekend_Late_Night  \\\n",
       "labels                                                            \n",
       "0                  159382.0         17493.0              1500.0   \n",
       "1                  202127.0         70284.0              6942.0   \n",
       "2                   49944.0         11092.0               913.0   \n",
       "3                  104753.0          8198.0               588.0   \n",
       "4                   93711.0         36203.0              2857.0   \n",
       "5                  129068.0         53747.0              4885.0   \n",
       "\n",
       "Concat_Tag  Weekend_Lunch  Weekend_Morning  \n",
       "labels                                      \n",
       "0                 52730.0          93101.0  \n",
       "1                179632.0         120485.0  \n",
       "2                 43955.0          21957.0  \n",
       "3                 23192.0          37939.0  \n",
       "4                104924.0          50939.0  \n",
       "5                167108.0          77719.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting cluster and checking the labels\n",
    "kmeans_2 = KMeans(n_clusters=6)\n",
    "kmeans_2.fit(cluster_2)\n",
    "\n",
    "cluster_2['labels'] = kmeans_2.labels_\n",
    "cluster_2.groupby('labels').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6s0PhgkkmxQ7",
    "outputId": "ae29e55a-b11f-4fbb-e6fa-27e5a272d338"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jain.ka/.conda/envs/custom/lib/python3.7/site-packages/ipykernel_launcher.py:8: UserWarning: \n",
      "The palette list has fewer values (4) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PC1', ylabel='PC2'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using PCA to visualise the data\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(cluster_2)\n",
    "# pca_samples = pca.transform(cluster_data)\n",
    "pca_samples_2 = pd.DataFrame(pca.fit_transform(cluster_2),columns=['PC1','PC2']) \n",
    "pca_samples_2['cluster'] = pd.Categorical(kmeans_2.labels_)\n",
    "\n",
    "sns.scatterplot(x=\"PC1\",y=\"PC2\",hue=\"cluster\",data=pca_samples_2,\n",
    "               palette=['green','orange','dodgerblue','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9_luJ1axmxQ7",
    "outputId": "67559dcd-5a92-46ce-f7dc-ff4305841578"
   },
   "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>Concat_Tag</th>\n",
       "      <th>Weekday_Dinner</th>\n",
       "      <th>Weekday_Late_Night</th>\n",
       "      <th>Weekday_Lunch</th>\n",
       "      <th>Weekday_Morning</th>\n",
       "      <th>Weekend_Dinner</th>\n",
       "      <th>Weekend_Late_Night</th>\n",
       "      <th>Weekend_Lunch</th>\n",
       "      <th>Weekend_Morning</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Concat_Tag  Weekday_Dinner  Weekday_Late_Night  Weekday_Lunch  \\\n",
       "user_id                                                         \n",
       "1                      0.0                 0.0            2.0   \n",
       "2                      0.0                 0.0            1.0   \n",
       "3                      2.0                 0.0            3.0   \n",
       "4                      0.0                 0.0            4.0   \n",
       "5                      1.0                 0.0            1.0   \n",
       "\n",
       "Concat_Tag  Weekday_Morning  Weekend_Dinner  Weekend_Late_Night  \\\n",
       "user_id                                                           \n",
       "1                       6.0             0.0                 0.0   \n",
       "2                       8.0             0.0                 0.0   \n",
       "3                       0.0             1.0                 0.0   \n",
       "4                       2.0             0.0                 0.0   \n",
       "5                       0.0             1.0                 0.0   \n",
       "\n",
       "Concat_Tag  Weekend_Lunch  Weekend_Morning  labels  \n",
       "user_id                                             \n",
       "1                     2.0              1.0       0  \n",
       "2                     1.0              5.0       4  \n",
       "3                     7.0              0.0       0  \n",
       "4                     0.0              0.0       0  \n",
       "5                     1.0              1.0       0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PZpoyONcmxQ7"
   },
   "outputs": [],
   "source": [
    "cluster_2.reset_index().groupby('labels').sum().to_csv('clustering.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zwHoiVpImxQ8"
   },
   "source": [
    "### Recommendation Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6VXry0C_mxQ8"
   },
   "outputs": [],
   "source": [
    "order_products_prior_df.fillna(0,inplace=True)\n",
    "order_products_train_df.fillna(0,inplace=True)\n",
    "orders_df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JiEnCsgFmxQ8"
   },
   "outputs": [],
   "source": [
    "data_merged = pd.concat([order_products_train_df, order_products_prior_df])\n",
    "orders = orders_df[['order_id','user_id']]\n",
    "\n",
    "data_merged = pd.merge(data_merged, orders, on='order_id').drop('order_id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pe6Rcqo-mxQ8"
   },
   "outputs": [],
   "source": [
    "data_merged = data_merged.join(pd.DataFrame({'BoughtFlag': [1]*len(data_merged)}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "smay8G1xmxQ8"
   },
   "outputs": [],
   "source": [
    "total_bought = data_merged.groupby(['user_id','product_id']).BoughtFlag.sum()\n",
    "data_merged = pd.merge(data_merged,total_bought, on=['user_id','product_id'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d_Ygsb3rmxQ8"
   },
   "outputs": [],
   "source": [
    "data_merged = data_merged[data_merged.BoughtFlag_y>0]\n",
    "data_merged['target'] = data_merged['BoughtFlag_y']\n",
    "data_merged.drop(['BoughtFlag_x','BoughtFlag_y','add_to_cart_order'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jjoJDGfPmxQ8"
   },
   "outputs": [],
   "source": [
    "data_merged.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VcKB7qLCmxQ8"
   },
   "outputs": [],
   "source": [
    "relation = data_merged[['user_id','product_id']].copy()\n",
    "relation.drop_duplicates(inplace=True)\n",
    "\n",
    "test = orders_df[orders_df.eval_set=='test']\n",
    "test = test[['user_id']]\n",
    "test_data = turicreate.SFrame(test)\n",
    "relation_data = turicreate.SFrame(relation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6YpNMcHvmxQ8"
   },
   "outputs": [],
   "source": [
    "train_data = turicreate.SFrame(data_merged)\n",
    "test_data = turicreate.SFrame(test)\n",
    "relation_data = turicreate.SFrame(relation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YxOXviF-mxQ8",
    "outputId": "0c79b05f-fa4a-4c19-becc-cd2fc770d0f9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Warning: Ignoring columns reordered;</pre>"
      ],
      "text/plain": [
       "Warning: Ignoring columns reordered;"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    To use these columns in scoring predictions, use a model that allows the use of additional features.</pre>"
      ],
      "text/plain": [
       "    To use these columns in scoring predictions, use a model that allows the use of additional features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 19428226 observations with 206209 users and 49685 items.</pre>"
      ],
      "text/plain": [
       "    Data has 19428226 observations with 206209 users and 49685 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 25.6631s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 25.6631s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training model from provided data.</pre>"
      ],
      "text/plain": [
       "Training model from provided data."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Gathering per-item and per-user statistics.</pre>"
      ],
      "text/plain": [
       "Gathering per-item and per-user statistics."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Item Statistics) | % Complete |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Item Statistics) | % Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2.927ms                        | 0.25       |</pre>"
      ],
      "text/plain": [
       "| 2.927ms                        | 0.25       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 255.684ms                      | 100        |</pre>"
      ],
      "text/plain": [
       "| 255.684ms                      | 100        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Setting up lookup tables.</pre>"
      ],
      "text/plain": [
       "Setting up lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Processing data in 2 passes using dense lookup tables.</pre>"
      ],
      "text/plain": [
       "Processing data in 2 passes using dense lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5.07s                               | 0                | 0               |</pre>"
      ],
      "text/plain": [
       "| 5.07s                               | 0                | 0               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8.07s                               | 14.5             | 7228            |</pre>"
      ],
      "text/plain": [
       "| 8.07s                               | 14.5             | 7228            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11.07s                              | 28.25            | 14128           |</pre>"
      ],
      "text/plain": [
       "| 11.07s                              | 28.25            | 14128           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 14.07s                              | 42               | 20921           |</pre>"
      ],
      "text/plain": [
       "| 14.07s                              | 42               | 20921           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22.72s                              | 49.75            | 24840           |</pre>"
      ],
      "text/plain": [
       "| 22.72s                              | 49.75            | 24840           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 23.96s                              | 100              | 49685           |</pre>"
      ],
      "text/plain": [
       "| 23.96s                              | 100              | 49685           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finalizing lookup tables.</pre>"
      ],
      "text/plain": [
       "Finalizing lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Generating candidate set for working with new users.</pre>"
      ],
      "text/plain": [
       "Generating candidate set for working with new users."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished training in 27.717s</pre>"
      ],
      "text/plain": [
       "Finished training in 27.717s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recommender =  turicreate.recommender.item_similarity_recommender.create(train_data, user_id='user_id', item_id='product_id', target='target', similarity_type='cosine', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HQZPOUJNmxQ8",
    "outputId": "97a2868f-e0f0-45c7-8f94-54b7baf33774"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 1000/75000 queries. users per second: 12470.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 1000/75000 queries. users per second: 12470.7"
      ]
     },
     "metadata": {},
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    {
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       "<pre>recommendations finished on 2000/75000 queries. users per second: 14336.2</pre>"
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       "<pre>recommendations finished on 4000/75000 queries. users per second: 15984</pre>"
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   ],
   "source": [
    "item_sim_result = recommender.recommend(users=test_data, items=relation_data, exclude_known=False, k=len(products_df)).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SUc2WuvfmxQ8"
   },
   "outputs": [],
   "source": [
    "def write_csv(results, orders_df, csv_name):\n",
    "    results = pd.merge(results,orders_df[orders_df.eval_set=='test'][['user_id', 'order_id']], on=['user_id'], how='left')\n",
    "    thresholds = {\n",
    "        'no': results.score.min()-1,\n",
    "        'std' : results.score.std(),\n",
    "        'zero' : 0,\n",
    "        'q05' : results.score.quantile(q=0.05),\n",
    "        'q10' : results.score.quantile(q=0.1)\n",
    "        }\n",
    "\n",
    "    results_dict = {}\n",
    "    for key,item in thresholds.items():\n",
    "        results_dict[key] = results[results['score'] > item]\n",
    "        results_dict[key] = results_dict[key].groupby('order_id')['product_id'].apply(list).reset_index(name='products')\n",
    "        results_dict[key]['products'] = results_dict[key].apply(clean_prediction, axis=1)\n",
    "\n",
    "    for key,item in thresholds.items():\n",
    "        if len(results_dict[key]) == 75000:\n",
    "            results_dict[key].to_csv('submission_turi_'+csv_name+'_pol'+str(key)+'.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b3KRU7LnmxQ9"
   },
   "outputs": [],
   "source": [
    "def clean_prediction(row):\n",
    "    data = row.products\n",
    "    data = str(\"\".join(str(data))[1:-1].replace(',',' '))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "APhpE8ummxQ9"
   },
   "outputs": [],
   "source": [
    "write_csv(item_sim_result, orders_df, 'item_sim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sPxWSqWfmxQ9",
    "outputId": "7b5e603d-c2b8-4944-ea20-c2061893cc0b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 19428226 observations with 206209 users and 49685 items.</pre>"
      ],
      "text/plain": [
       "    Data has 19428226 observations with 206209 users and 49685 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 22.404s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 22.404s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 8        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 8        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-08    |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-08    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
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      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
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       "  Optimizing model using SGD; tuning step size."
      ]
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      "text/html": [
       "<pre>  Using 2428528 / 19428226 points for tuning the step size.</pre>"
      ],
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       "  Using 2428528 / 19428226 points for tuning the step size."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
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       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 16.6667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 16.6667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 4.16667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 4.16667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.04167           | 6.09133                                  |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.04167           | 6.09133                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.520833          | 6.48598                                  |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.520833          | 6.48598                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.260417          | 9.88838                                  |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.260417          | 9.88838                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.130208          | 12.1629                                  |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.130208          | 12.1629                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 1.04167           | 6.09133                                  |</pre>"
      ],
      "text/plain": [
       "| Final   | 1.04167           | 6.09133                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
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    },
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      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
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       "Starting Optimization."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 95us         | 16.7061           | 4.08731               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 95us         | 16.7061           | 4.08731               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 8.80s        | 13.5632           | 3.68039               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 1       | 8.80s        | 13.5632           | 3.68039               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 17.69s       | 11.6473           | 3.41081               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 2       | 17.69s       | 11.6473           | 3.41081               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 26.60s       | 10.8189           | 3.28739               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 3       | 26.60s       | 10.8189           | 3.28739               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 35.64s       | 10.1865           | 3.18983               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 4       | 35.64s       | 10.1865           | 3.18983               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 45.11s       | 9.83273           | 3.1339                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 5       | 45.11s       | 9.83273           | 3.1339                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 1m 2s        | 9.60322           | 3.09707               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 7       | 1m 2s        | 9.60322           | 3.09707               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 1m 11s       | 9.51756           | 3.0832                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 8       | 1m 11s       | 9.51756           | 3.0832                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9       | 1m 20s       | 9.39042           | 3.06251               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 9       | 1m 20s       | 9.39042           | 3.06251               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 1m 29s       | 9.29033           | 3.04611               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 10      | 1m 29s       | 9.29033           | 3.04611               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 1m 38s       | 9.2278            | 3.03582               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 11      | 1m 38s       | 9.2278            | 3.03582               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 1m 47s       | 9.15071           | 3.02308               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 12      | 1m 47s       | 9.15071           | 3.02308               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 13      | 1m 56s       | 9.11058           | 3.01642               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 13      | 1m 56s       | 9.11058           | 3.01642               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 14      | 2m 5s        | 9.0466            | 3.00578               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 14      | 2m 5s        | 9.0466            | 3.00578               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 2m 14s       | 9.0124            | 3.00007               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 15      | 2m 14s       | 9.0124            | 3.00007               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16      | 2m 23s       | 8.99491           | 2.99714               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 16      | 2m 23s       | 8.99491           | 2.99714               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 17      | 2m 32s       | 8.95418           | 2.99033               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 17      | 2m 32s       | 8.95418           | 2.99033               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 18      | 2m 41s       | 8.90856           | 2.98267               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 18      | 2m 41s       | 8.90856           | 2.98267               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 19      | 2m 50s       | 8.88811           | 2.97923               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 19      | 2m 50s       | 8.88811           | 2.97923               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 2m 59s       | 8.86268           | 2.97495               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 20      | 2m 59s       | 8.86268           | 2.97495               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 21      | 3m 7s        | 8.82754           | 2.96902               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 21      | 3m 7s        | 8.82754           | 2.96902               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22      | 3m 16s       | 8.82833           | 2.96915               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 22      | 3m 16s       | 8.82833           | 2.96915               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 23      | 3m 25s       | 8.79095           | 2.96283               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 23      | 3m 25s       | 8.79095           | 2.96283               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 24      | 3m 34s       | 8.76753           | 2.95887               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 24      | 3m 34s       | 8.76753           | 2.95887               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 3m 43s       | 8.76415           | 2.95829               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 25      | 3m 43s       | 8.76415           | 2.95829               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 26      | 3m 52s       | 8.7487            | 2.95566               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 26      | 3m 52s       | 8.7487            | 2.95566               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 27      | 4m 1s        | 8.74208           | 2.95453               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 27      | 4m 1s        | 8.74208           | 2.95453               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 28      | 4m 10s       | 8.71225           | 2.94947               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 28      | 4m 10s       | 8.71225           | 2.94947               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 29      | 4m 18s       | 8.72033           | 2.95084               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 29      | 4m 18s       | 8.72033           | 2.95084               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 4m 28s       | 8.69391           | 2.94635               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 30      | 4m 28s       | 8.69391           | 2.94635               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 31      | 4m 37s       | 8.69087           | 2.94582               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 31      | 4m 37s       | 8.69087           | 2.94582               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 32      | 4m 46s       | 8.68242           | 2.94438               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 32      | 4m 46s       | 8.68242           | 2.94438               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 33      | 4m 55s       | 8.67278           | 2.94274               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 33      | 4m 55s       | 8.67278           | 2.94274               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 34      | 5m 4s        | 8.67811           | 2.94364               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 34      | 5m 4s        | 8.67811           | 2.94364               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 5m 13s       | 8.67195           | 2.94258               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 35      | 5m 13s       | 8.67195           | 2.94258               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 36      | 5m 22s       | 8.67385           | 2.9429                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 36      | 5m 22s       | 8.67385           | 2.9429                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 37      | 5m 31s       | 8.67236           | 2.94264               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 37      | 5m 31s       | 8.67236           | 2.94264               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 38      | 5m 40s       | 8.64697           | 2.93832               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 38      | 5m 40s       | 8.64697           | 2.93832               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 39      | 5m 49s       | 8.65966           | 2.94047               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 39      | 5m 49s       | 8.65966           | 2.94047               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 5m 57s       | 8.62974           | 2.93538               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 40      | 5m 57s       | 8.62974           | 2.93538               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 41      | 6m 6s        | 8.62484           | 2.93453               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 41      | 6m 6s        | 8.62484           | 2.93453               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 42      | 6m 15s       | 8.62805           | 2.93508               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 42      | 6m 15s       | 8.62805           | 2.93508               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 43      | 6m 24s       | 8.59375           | 2.92922               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 43      | 6m 24s       | 8.59375           | 2.92922               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 44      | 6m 33s       | 8.60855           | 2.93174               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 44      | 6m 33s       | 8.60855           | 2.93174               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 6m 42s       | 8.61833           | 2.9334                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 45      | 6m 42s       | 8.61833           | 2.9334                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 46      | 6m 50s       | 8.61788           | 2.93332               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 46      | 6m 50s       | 8.61788           | 2.93332               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 47      | 6m 59s       | 8.62129           | 2.9339                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 47      | 6m 59s       | 8.62129           | 2.9339                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 48      | 7m 8s        | 8.61898           | 2.9335                | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 48      | 7m 8s        | 8.61898           | 2.9335                | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 49      | 7m 18s       | 8.63842           | 2.93681               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 49      | 7m 18s       | 8.63842           | 2.93681               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 7m 27s       | 8.65525           | 2.93967               | 1.04167     |</pre>"
      ],
      "text/plain": [
       "| 50      | 7m 27s       | 8.65525           | 2.93967               | 1.04167     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 8.43951</pre>"
      ],
      "text/plain": [
       "       Final objective value: 8.43951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 2.90275</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 2.90275"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_model = turicreate.factorization_recommender.create(train_data, user_id='user_id', \n",
    "max_iterations=50, item_id='product_id', target='target', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cXsn8RmimxQ9",
    "outputId": "728e4185-c2bc-4ea2-9df5-ef81667d6263"
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<pre>recommendations finished on 4000/75000 queries. users per second: 22304.7</pre>"
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      "text/html": [
       "<pre>recommendations finished on 5000/75000 queries. users per second: 22828.8</pre>"
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      "text/html": [
       "<pre>recommendations finished on 6000/75000 queries. users per second: 23532.9</pre>"
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      "text/html": [
       "<pre>recommendations finished on 9000/75000 queries. users per second: 22696.5</pre>"
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       "recommendations finished on 9000/75000 queries. users per second: 22696.5"
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       "recommendations finished on 10000/75000 queries. users per second: 23278.5"
      ]
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    },
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     "data": {
      "text/html": [
       "<pre>recommendations finished on 11000/75000 queries. users per second: 23662.9</pre>"
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       "recommendations finished on 11000/75000 queries. users per second: 23662.9"
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     "data": {
      "text/html": [
       "<pre>recommendations finished on 12000/75000 queries. users per second: 23892.6</pre>"
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       "recommendations finished on 12000/75000 queries. users per second: 23892.6"
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      "text/html": [
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    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 20000/75000 queries. users per second: 23753.3</pre>"
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       "recommendations finished on 20000/75000 queries. users per second: 23753.3"
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      "text/html": [
       "<pre>recommendations finished on 21000/75000 queries. users per second: 23658.1</pre>"
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       "recommendations finished on 21000/75000 queries. users per second: 23658.1"
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    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 22000/75000 queries. users per second: 23740.2</pre>"
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    },
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     "data": {
      "text/html": [
       "<pre>recommendations finished on 23000/75000 queries. users per second: 23802.5</pre>"
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       "recommendations finished on 23000/75000 queries. users per second: 23802.5"
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    },
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     "data": {
      "text/html": [
       "<pre>recommendations finished on 24000/75000 queries. users per second: 23663.2</pre>"
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       "recommendations finished on 24000/75000 queries. users per second: 23663.2"
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     "data": {
      "text/html": [
       "<pre>recommendations finished on 25000/75000 queries. users per second: 23836.3</pre>"
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      "text/html": [
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      "text/html": [
       "<pre>recommendations finished on 28000/75000 queries. users per second: 23470</pre>"
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      "text/html": [
       "<pre>recommendations finished on 30000/75000 queries. users per second: 23501.6</pre>"
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       "recommendations finished on 30000/75000 queries. users per second: 23501.6"
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      "text/html": [
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       "<pre>recommendations finished on 58000/75000 queries. users per second: 24309.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 58000/75000 queries. users per second: 24309.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 59000/75000 queries. users per second: 24412.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 59000/75000 queries. users per second: 24412.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 60000/75000 queries. users per second: 24465.5</pre>"
      ],
      "text/plain": [
       "recommendations finished on 60000/75000 queries. users per second: 24465.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 61000/75000 queries. users per second: 24468</pre>"
      ],
      "text/plain": [
       "recommendations finished on 61000/75000 queries. users per second: 24468"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 62000/75000 queries. users per second: 24510.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 62000/75000 queries. users per second: 24510.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 63000/75000 queries. users per second: 24432.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 63000/75000 queries. users per second: 24432.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 64000/75000 queries. users per second: 24412.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 64000/75000 queries. users per second: 24412.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 65000/75000 queries. users per second: 24470.5</pre>"
      ],
      "text/plain": [
       "recommendations finished on 65000/75000 queries. users per second: 24470.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 66000/75000 queries. users per second: 24511.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 66000/75000 queries. users per second: 24511.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 67000/75000 queries. users per second: 24600.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 67000/75000 queries. users per second: 24600.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 68000/75000 queries. users per second: 24593.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 68000/75000 queries. users per second: 24593.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 69000/75000 queries. users per second: 24653.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 69000/75000 queries. users per second: 24653.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 70000/75000 queries. users per second: 24626.6</pre>"
      ],
      "text/plain": [
       "recommendations finished on 70000/75000 queries. users per second: 24626.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 71000/75000 queries. users per second: 24703.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 71000/75000 queries. users per second: 24703.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 72000/75000 queries. users per second: 24778.5</pre>"
      ],
      "text/plain": [
       "recommendations finished on 72000/75000 queries. users per second: 24778.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 73000/75000 queries. users per second: 24862.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 73000/75000 queries. users per second: 24862.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 74000/75000 queries. users per second: 24998.6</pre>"
      ],
      "text/plain": [
       "recommendations finished on 74000/75000 queries. users per second: 24998.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 75000/75000 queries. users per second: 24994.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 75000/75000 queries. users per second: 24994.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_results = f_model.recommend(users=test_data, items=relation_data, exclude_known=False, k=len(products_df)).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5KAqzRrRmxQ9"
   },
   "outputs": [],
   "source": [
    "write_csv(f_results, orders_df, 'f_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3ZI3at3EmxQ9",
    "outputId": "3f1553b8-f61b-45ee-d821-d156f12c5f22"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 19428226 observations with 206209 users and 49685 items.</pre>"
      ],
      "text/plain": [
       "    Data has 19428226 observations with 206209 users and 49685 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 19.991s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 19.991s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training ranking_factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training ranking_factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 32       |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 32       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-09    |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-09    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | adagrad  |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | adagrad  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |</pre>"
      ],
      "text/plain": [
       "| ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>  Using 2428528 / 19428226 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 2428528 / 19428226 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 16.6667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 0       | 16.6667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 4.16667           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 1       | 4.16667           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.04167           | Not Viable                               |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.04167           | Not Viable                               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.260417          | No Decrease (34.5172 >= 32.7302)         |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.260417          | No Decrease (34.5172 >= 32.7302)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0651042         | 27.5042                                  |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0651042         | 27.5042                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.0325521         | 27.8805                                  |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.0325521         | 27.8805                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.016276          | 28.4313                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.016276          | 28.4313                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 0.00813802        | 29.3646                                  |</pre>"
      ],
      "text/plain": [
       "| 7       | 0.00813802        | 29.3646                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.0651042         | 27.5042                                  |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.0651042         | 27.5042                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
      ],
      "text/plain": [
       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Initial | 122us        | 32.7518           | 4.08731               |             |</pre>"
      ],
      "text/plain": [
       "| Initial | 122us        | 32.7518           | 4.08731               |             |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 25.27s       | 25.0357           | 3.98038               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 1       | 25.27s       | 25.0357           | 3.98038               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 50.33s       | 24.334            | 4.03844               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 2       | 50.33s       | 24.334            | 4.03844               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 1m 16s       | 23.7902           | 3.92795               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 3       | 1m 16s       | 23.7902           | 3.92795               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 1m 42s       | 23.2193           | 3.88897               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 4       | 1m 42s       | 23.2193           | 3.88897               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 2m 9s        | 22.8244           | 3.87067               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 5       | 2m 9s        | 22.8244           | 3.87067               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7       | 3m 2s        | 22.2855           | 3.84224               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 7       | 3m 2s        | 22.2855           | 3.84224               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8       | 3m 28s       | 22.0936           | 3.83018               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 8       | 3m 28s       | 22.0936           | 3.83018               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9       | 3m 54s       | 21.9215           | 3.81905               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 9       | 3m 54s       | 21.9215           | 3.81905               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10      | 4m 20s       | 21.7754           | 3.80902               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 10      | 4m 20s       | 21.7754           | 3.80902               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 11      | 4m 46s       | 21.646            | 3.79935               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 11      | 4m 46s       | 21.646            | 3.79935               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 12      | 5m 13s       | 21.5313           | 3.79095               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 12      | 5m 13s       | 21.5313           | 3.79095               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 13      | 5m 39s       | 21.4223           | 3.7828                | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 13      | 5m 39s       | 21.4223           | 3.7828                | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 14      | 6m 6s        | 21.328            | 3.77506               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 14      | 6m 6s        | 21.328            | 3.77506               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 15      | 6m 32s       | 21.2374           | 3.76768               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 15      | 6m 32s       | 21.2374           | 3.76768               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 16      | 6m 59s       | 21.15             | 3.76085               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 16      | 6m 59s       | 21.15             | 3.76085               | 0.0651042   |"
      ]
     },
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    {
     "data": {
      "text/html": [
       "<pre>| 17      | 7m 25s       | 21.0722           | 3.75395               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 17      | 7m 25s       | 21.0722           | 3.75395               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 18      | 7m 51s       | 20.9985           | 3.74778               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 18      | 7m 51s       | 20.9985           | 3.74778               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 19      | 8m 17s       | 20.9291           | 3.74127               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 19      | 8m 17s       | 20.9291           | 3.74127               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 20      | 8m 43s       | 20.8575           | 3.73507               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 20      | 8m 43s       | 20.8575           | 3.73507               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 21      | 9m 10s       | 20.7894           | 3.72903               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 21      | 9m 10s       | 20.7894           | 3.72903               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 22      | 9m 36s       | 20.7272           | 3.72327               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 22      | 9m 36s       | 20.7272           | 3.72327               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 23      | 10m 2s       | 20.6696           | 3.71732               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 23      | 10m 2s       | 20.6696           | 3.71732               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 24      | 10m 28s      | 20.6093           | 3.7121                | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 24      | 10m 28s      | 20.6093           | 3.7121                | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 25      | 10m 55s      | 20.5525           | 3.70681               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 25      | 10m 55s      | 20.5525           | 3.70681               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 26      | 11m 21s      | 20.5001           | 3.70166               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 26      | 11m 21s      | 20.5001           | 3.70166               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 27      | 11m 47s      | 20.449            | 3.69685               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 27      | 11m 47s      | 20.449            | 3.69685               | 0.0651042   |"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 28      | 12m 13s      | 20.4018           | 3.69215               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 28      | 12m 13s      | 20.4018           | 3.69215               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 29      | 12m 39s      | 20.3512           | 3.68766               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 29      | 12m 39s      | 20.3512           | 3.68766               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 30      | 13m 6s       | 20.3024           | 3.68306               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 30      | 13m 6s       | 20.3024           | 3.68306               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 31      | 13m 33s      | 20.2563           | 3.67845               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 31      | 13m 33s      | 20.2563           | 3.67845               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 32      | 14m 0s       | 20.2139           | 3.67416               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 32      | 14m 0s       | 20.2139           | 3.67416               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 33      | 14m 26s      | 20.1691           | 3.66991               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 33      | 14m 26s      | 20.1691           | 3.66991               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 34      | 14m 52s      | 20.1252           | 3.66555               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 34      | 14m 52s      | 20.1252           | 3.66555               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 35      | 15m 18s      | 20.0811           | 3.66134               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 35      | 15m 18s      | 20.0811           | 3.66134               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 36      | 15m 45s      | 20.0376           | 3.65682               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 36      | 15m 45s      | 20.0376           | 3.65682               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 37      | 16m 11s      | 19.9987           | 3.65306               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 37      | 16m 11s      | 19.9987           | 3.65306               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 38      | 16m 37s      | 19.9567           | 3.64865               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 38      | 16m 37s      | 19.9567           | 3.64865               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 39      | 17m 4s       | 19.9158           | 3.64478               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 39      | 17m 4s       | 19.9158           | 3.64478               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 40      | 17m 30s      | 19.8746           | 3.64051               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 40      | 17m 30s      | 19.8746           | 3.64051               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 41      | 17m 57s      | 19.8341           | 3.63673               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 41      | 17m 57s      | 19.8341           | 3.63673               | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 42      | 18m 23s      | 19.7978           | 3.6328                | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 42      | 18m 23s      | 19.7978           | 3.6328                | 0.0651042   |"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 43      | 18m 49s      | 19.7575           | 3.62877               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 43      | 18m 49s      | 19.7575           | 3.62877               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 44      | 19m 16s      | 19.7196           | 3.62527               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 44      | 19m 16s      | 19.7196           | 3.62527               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 45      | 19m 42s      | 19.6835           | 3.62146               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 45      | 19m 42s      | 19.6835           | 3.62146               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 46      | 20m 8s       | 19.6487           | 3.61753               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 46      | 20m 8s       | 19.6487           | 3.61753               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 47      | 20m 35s      | 19.6114           | 3.6141                | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 47      | 20m 35s      | 19.6114           | 3.6141                | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 48      | 21m 1s       | 19.5743           | 3.61016               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 48      | 21m 1s       | 19.5743           | 3.61016               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 49      | 21m 27s      | 19.5391           | 3.60688               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 49      | 21m 27s      | 19.5391           | 3.60688               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 50      | 21m 54s      | 19.5018           | 3.60329               | 0.0651042   |</pre>"
      ],
      "text/plain": [
       "| 50      | 21m 54s      | 19.5018           | 3.60329               | 0.0651042   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 19.2825</pre>"
      ],
      "text/plain": [
       "       Final objective value: 19.2825"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 3.57267</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 3.57267"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_model = turicreate.ranking_factorization_recommender.create(train_data, user_id='user_id', \n",
    "max_iterations=50, item_id='product_id', target='target', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-PX38fC7mxQ9",
    "outputId": "1c5740b1-b793-452f-8f35-280c96751479"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 1000/75000 queries. users per second: 15395.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 1000/75000 queries. users per second: 15395.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 2000/75000 queries. users per second: 18815.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 2000/75000 queries. users per second: 18815.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 3000/75000 queries. users per second: 21429.5</pre>"
      ],
      "text/plain": [
       "recommendations finished on 3000/75000 queries. users per second: 21429.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 4000/75000 queries. users per second: 20792.1</pre>"
      ],
      "text/plain": [
       "recommendations finished on 4000/75000 queries. users per second: 20792.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 5000/75000 queries. users per second: 21771.1</pre>"
      ],
      "text/plain": [
       "recommendations finished on 5000/75000 queries. users per second: 21771.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 6000/75000 queries. users per second: 22533.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 6000/75000 queries. users per second: 22533.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 7000/75000 queries. users per second: 23176.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 7000/75000 queries. users per second: 23176.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 8000/75000 queries. users per second: 23728.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 8000/75000 queries. users per second: 23728.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 9000/75000 queries. users per second: 24111.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 9000/75000 queries. users per second: 24111.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 10000/75000 queries. users per second: 24282.3</pre>"
      ],
      "text/plain": [
       "recommendations finished on 10000/75000 queries. users per second: 24282.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 11000/75000 queries. users per second: 24300.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 11000/75000 queries. users per second: 24300.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 12000/75000 queries. users per second: 24406.4</pre>"
      ],
      "text/plain": [
       "recommendations finished on 12000/75000 queries. users per second: 24406.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 13000/75000 queries. users per second: 24639.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 13000/75000 queries. users per second: 24639.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 14000/75000 queries. users per second: 24666.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 14000/75000 queries. users per second: 24666.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 15000/75000 queries. users per second: 25001.3</pre>"
      ],
      "text/plain": [
       "recommendations finished on 15000/75000 queries. users per second: 25001.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 16000/75000 queries. users per second: 25098.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 16000/75000 queries. users per second: 25098.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 17000/75000 queries. users per second: 25149.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 17000/75000 queries. users per second: 25149.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 18000/75000 queries. users per second: 25039.6</pre>"
      ],
      "text/plain": [
       "recommendations finished on 18000/75000 queries. users per second: 25039.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 19000/75000 queries. users per second: 25133.8</pre>"
      ],
      "text/plain": [
       "recommendations finished on 19000/75000 queries. users per second: 25133.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 20000/75000 queries. users per second: 25134.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 20000/75000 queries. users per second: 25134.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 21000/75000 queries. users per second: 25229.2</pre>"
      ],
      "text/plain": [
       "recommendations finished on 21000/75000 queries. users per second: 25229.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 22000/75000 queries. users per second: 25317.7</pre>"
      ],
      "text/plain": [
       "recommendations finished on 22000/75000 queries. users per second: 25317.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>recommendations finished on 23000/75000 queries. users per second: 25551.9</pre>"
      ],
      "text/plain": [
       "recommendations finished on 23000/75000 queries. users per second: 25551.9"
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   ],
   "source": [
    "rf_results = rf_model.recommend(users=test_data, items=relation_data, \n",
    "                                exclude_known=False, k=len(products_df)).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QnvBXDYxmxQ9"
   },
   "outputs": [],
   "source": [
    "write_csv(rf_results, orders_df, 'rf_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ESTkRf8kmxQ9",
    "outputId": "8156ea66-0ec2-42bb-c7c3-ab26db9aec04"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Warning: Ignoring columns reordered;</pre>"
      ],
      "text/plain": [
       "Warning: Ignoring columns reordered;"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    To use these columns in scoring predictions, use a model that allows the use of additional features.</pre>"
      ],
      "text/plain": [
       "    To use these columns in scoring predictions, use a model that allows the use of additional features."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 19428226 observations with 206209 users and 49685 items.</pre>"
      ],
      "text/plain": [
       "    Data has 19428226 observations with 206209 users and 49685 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 19.4237s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 19.4237s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>19428226 observations to process; with 49685 unique items.</pre>"
      ],
      "text/plain": [
       "19428226 observations to process; with 49685 unique items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_model = turicreate.recommender.popularity_recommender.create(train_data, user_id='user_id', item_id='product_id', target='target', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dJmjI8pamxQ9",
    "outputId": "b79cdafb-5a69-4ec6-81b2-609bf4967c6b"
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "pr_results = pr_model.recommend(users=test_data, items=relation_data,exclude_known=False, k=len(products_df)).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KuxbwIZUmxQ9"
   },
   "outputs": [],
   "source": [
    "write_csv(pr_results, orders_df, 'pr_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MfCr7QN4mxQ9"
   },
   "outputs": [],
   "source": [
    "# order_products_prior_df = order_products_prior_df.append(order_products_train_df)\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, orders_df, on='order_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ESakvSO4mxQ-"
   },
   "outputs": [],
   "source": [
    "unique_product_id = order_products_prior_df.product_id.unique()\n",
    "unique_user_id = order_products_prior_df.user_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pb8OjPZfmxQ-"
   },
   "outputs": [],
   "source": [
    "order_products_prior_df = order_products_prior_df.join(pd.DataFrame({'BoughtFlag': [1]*len(order_products_prior_df)}))"
   ]
  }
 ],
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