{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22d208b8",
   "metadata": {},
   "source": [
    "## Procedimiento Estándar de Clustering (Kaggle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416b5dfe",
   "metadata": {},
   "source": [
    "# I. Análisis de clientes de un Centro Comercial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df487604",
   "metadata": {},
   "source": [
    "### Modificado de E. Inzaugarat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b72e0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09385481",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9e85b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "808678c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3cc1cf30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "371fd8b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "839ded0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import extra_graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e75db8",
   "metadata": {},
   "source": [
    "## DataSet del Centro Comercial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "363d53bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Mall_Customers.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-d1a185fcac0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcustomers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Mall_Customers.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcustomers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             )\n\u001b[1;32m   1049\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m         \"\"\"\n\u001b[0;32m-> 1362\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Mall_Customers.csv'"
     ]
    }
   ],
   "source": [
    "customers = pd.read_csv(\"Mall_Customers.csv\")\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7dc6f801-42bd-436d-a88a-20b9ce917806",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'customers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-10c64e0f2afe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcustomers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'customers' is not defined"
     ]
    }
   ],
   "source": [
    "customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2cfcfa",
   "metadata": {},
   "source": [
    "k-means no permite datos nulos, por lo que hay que localizar estos ejemplos y decidir su tratamiento (si son pocos, generalmente se eliminan). En este caso, no hay variables con datos faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86dcab4a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'customers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-acc8837f5cf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Datos perdidos en cada variable: \\n{customers.isnull().sum()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'customers' is not defined"
     ]
    }
   ],
   "source": [
    "print(f\"Datos perdidos en cada variable: \\n{customers.isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fee0dca",
   "metadata": {},
   "source": [
    "k-means solo admite variables en una escala de rango o de proporción, ya que se basa en distancias entre los ejemplos. Si se requiere considerar variables categóricas hay que transformarlas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "683efe8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Variables:                Tipo: \\n{customers.dtypes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44be6ff3",
   "metadata": {},
   "source": [
    "El género (Gender) es categórica y se procede a su transformación, haciendo 0 a Male y 1 a Femane."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc44f0f",
   "metadata": {},
   "source": [
    "## Exploración de datos: estadística descriptiva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "062e4c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistics(variable):\n",
    "    if variable.dtype == \"int64\" or variable.dtype == \"float64\":\n",
    "        return pd.DataFrame([[variable.name, np.mean(variable), np.std(variable), np.median(variable), np.var(variable)]], \n",
    "                            columns = [\"Variable\", \"Mean\", \"Standard Deviation\", \"Median\", \"Variance\"]).set_index(\"Variable\")\n",
    "    else:\n",
    "        return pd.DataFrame(variable.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0daa4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def histplot(x):\n",
    "    if x.dtype == \"int64\" or x.dtype == \"float64\":\n",
    "        # Select size of bins by getting maximum and minimum and divide the substraction by 10\n",
    "        size_bins = 10\n",
    "        # Get the title by getting the name of the column\n",
    "        title = x.name\n",
    "        #Assign random colors to each graph\n",
    "        color_kde = list(map(float, np.random.rand(3,)))\n",
    "        color_bar = list(map(float, np.random.rand(3,)))\n",
    "\n",
    "        # Plot the displot\n",
    "        sns.distplot(x, bins=size_bins, kde_kws={\"lw\": 1.5, \"alpha\":0.8, \"color\":color_kde},\n",
    "                       hist_kws={\"linewidth\": 1.5, \"edgecolor\": \"grey\",\n",
    "                                \"alpha\": 0.4, \"color\":color_bar})\n",
    "        # Customize ticks and labels\n",
    "        plt.xticks(size=14)\n",
    "        plt.yticks(size=14);\n",
    "        plt.ylabel(\"Frecuencia\", size=16, labelpad=15);\n",
    "        # Customize title\n",
    "        plt.title(title, size=18)\n",
    "        # Customize grid and axes visibility\n",
    "        plt.grid(False);\n",
    "        plt.gca().spines[\"top\"].set_visible(False);\n",
    "        plt.gca().spines[\"right\"].set_visible(False);\n",
    "        plt.gca().spines[\"bottom\"].set_visible(False);\n",
    "        plt.gca().spines[\"left\"].set_visible(False);   \n",
    "    else:\n",
    "        x = pd.DataFrame(x)\n",
    "        # Plot       \n",
    "        sns.catplot(x=x.columns[0], kind=\"count\", palette=\"spring\", data=x)\n",
    "        # Customize title\n",
    "        title = x.columns[0]\n",
    "        plt.title(title, size=18)\n",
    "        # Customize ticks and labels\n",
    "        plt.xticks(size=14)\n",
    "        plt.yticks(size=14);\n",
    "        plt.xlabel(\"\")\n",
    "        plt.ylabel(\"Counts\", size=16, labelpad=15);        \n",
    "        # Customize grid and axes visibility\n",
    "        plt.gca().spines[\"top\"].set_visible(False);\n",
    "        plt.gca().spines[\"right\"].set_visible(False);\n",
    "        plt.gca().spines[\"bottom\"].set_visible(False);\n",
    "        plt.gca().spines[\"left\"].set_visible(False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7c4c798",
   "metadata": {},
   "outputs": [],
   "source": [
    "spending = customers[\"Spending Score (1-100)\"]\n",
    "# incomme = customers[\"Annual Income (k$)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f53d2ec",
   "metadata": {},
   "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>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <td>50.2</td>\n",
       "      <td>25.758882</td>\n",
       "      <td>50.0</td>\n",
       "      <td>663.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Mean  Standard Deviation  Median  Variance\n",
       "Variable                                                          \n",
       "Spending Score (1-100)  50.2           25.758882    50.0    663.52"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(spending)\n",
    "# statistics(age)\n",
    "# statistics(incomme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7da05eba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2f2cce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/josel/opt/anaconda3/envs/python/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n"
     ]
    },
    {
     "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": [
    "histplot(spending)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b7b9ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = customers[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c40dd56f",
   "metadata": {},
   "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>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>38.85</td>\n",
       "      <td>13.934041</td>\n",
       "      <td>36.0</td>\n",
       "      <td>194.1575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean  Standard Deviation  Median  Variance\n",
       "Variable                                             \n",
       "Age       38.85           13.934041    36.0  194.1575"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "410e67fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/josel/opt/anaconda3/envs/python/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histplot(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6b56cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "income = customers[\"Annual Income (k$)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2cb6df11",
   "metadata": {},
   "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>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <td>60.56</td>\n",
       "      <td>26.198977</td>\n",
       "      <td>61.5</td>\n",
       "      <td>686.3864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mean  Standard Deviation  Median  Variance\n",
       "Variable                                                       \n",
       "Annual Income (k$)  60.56           26.198977    61.5  686.3864"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1a5326cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/josel/opt/anaconda3/envs/python/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n"
     ]
    },
    {
     "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": [
    "histplot(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "733fab18",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = customers[\"Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d1db4727",
   "metadata": {},
   "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>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender\n",
       "Female     112\n",
       "Male        88"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42380b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histplot(gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaa4018",
   "metadata": {},
   "source": [
    "### Correlaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd9c1eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.125x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(customers, x_vars = [\"Age\", \"Annual Income (k$)\", \"Spending Score (1-100)\"], \n",
    "               y_vars = [\"Age\", \"Annual Income (k$)\", \"Spending Score (1-100)\"], \n",
    "               hue = \"Gender\", \n",
    "               kind= \"scatter\",\n",
    "               palette = \"YlGnBu\",\n",
    "               height = 2,\n",
    "               plot_kws={\"s\": 35, \"alpha\": 0.8});"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7b6132",
   "metadata": {},
   "source": [
    "## Reducción de dimensiones con PCA (proceso de ordenación)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49d83140",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[\"Male\"] = customers.Gender.apply(lambda x: 0 if x == \"Male\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6cc4a81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[\"Female\"] = customers.Gender.apply(lambda x: 0 if x == \"Female\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0fc0129e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = customers.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eafa0d25",
   "metadata": {},
   "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>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Male  Female\n",
       "0   19                  15                      39     0       1\n",
       "1   21                  15                      81     0       1\n",
       "2   20                  16                       6     1       0\n",
       "3   23                  16                      77     1       0\n",
       "4   31                  17                      40     1       0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9016e431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA and fit the features selected\n",
    "pca = PCA(n_components=2).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "406bb27a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.88980385e-01  5.88604475e-01  7.86022241e-01  3.32880772e-04\n",
      "  -3.32880772e-04]\n",
      " [ 1.30957602e-01  8.08400899e-01 -5.73875514e-01 -1.57927017e-03\n",
      "   1.57927017e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(pca.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1b8b8b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[700.26450987 684.33354753]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c7463eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform samples using the PCA fit\n",
    "pca_2d = pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f072b4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def biplot(score, coeff, labels=None):\n",
    "#    xs = score[:,0]\n",
    "#    ys = score[:,1]\n",
    "#    n = coeff.shape[0]\n",
    "#    scalex = 1.0/(xs.max()- xs.min())\n",
    "#    scaley = 1.0/(ys.max()- ys.min())\n",
    "#    plt.scatter(xs*scalex,ys*scaley, color=\"#c7e9c0\", edgecolor=\"#006d2c\", alpha=0.5)\n",
    "#    for i in range(n):\n",
    "#        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color='#253494',alpha=0.5,lw=2) \n",
    "#        if labels is None:\n",
    "#            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color=\"#000000\", ha=\"center\", va=\"center\")\n",
    "#        else:\n",
    "#            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color=\"#000000\", ha=\"center\", va=\"center\")\n",
    "#    plt.xlim(-.75,1)\n",
    "#    plt.ylim(-0.5,1)\n",
    "#    plt.grid(False)\n",
    "#    plt.xticks(np.arange(0, 1, 0.5), size=12)\n",
    "#    plt.yticks(np.arange(-0.75, 1, 0.5), size=12)\n",
    "#    plt.xlabel(\"Componente 1\", size=14)\n",
    "#    plt.ylabel(\"Componente 2\", size=14)\n",
    "#    plt.gca().spines[\"top\"].set_visible(False);\n",
    "#    plt.gca().spines[\"right\"].set_visible(False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "38821023",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Biplot\n",
    "\n",
    "def biplot(score, coeff, labels=X.columns):\n",
    "    xs = score[:,0]\n",
    "    ys = score[:,1]\n",
    "    n = coeff.shape[0]\n",
    "    scalex = 1.0/(xs.max()- xs.min())\n",
    "    scaley = 1.0/(ys.max()- ys.min())\n",
    "    plt.scatter(xs*scalex,ys*scaley, color=\"#c7e9c0\", edgecolor=\"#006d2c\", alpha=0.5)\n",
    "    for i in range(n):\n",
    "        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color='#253494',alpha=0.5,lw=2) \n",
    "        if labels is None:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color=\"#000000\", ha=\"center\", va=\"center\")\n",
    "        else:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color=\"#000000\", ha=\"center\", va=\"center\")\n",
    "    plt.xlim(-.75,1)\n",
    "    plt.ylim(-0.5,1)\n",
    "    plt.grid(False)\n",
    "    plt.xticks(np.arange(0, 1, 0.5), size=12)\n",
    "    plt.yticks(np.arange(-0.75, 1, 0.5), size=12)\n",
    "    plt.xlabel(\"Componente 1\", size=14)\n",
    "    plt.ylabel(\"Componente 2\", size=14)\n",
    "    plt.gca().spines[\"top\"].set_visible(False);\n",
    "    plt.gca().spines[\"right\"].set_visible(False);  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8cc7c89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "biplot(pca_2d[:,0:2],np.transpose(pca.components_[0:2, :]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ce51166",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra_graphs.biplot(pca_2d[:,0:2], np.transpose(pca.components_[0:2, :]), labels=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd60281",
   "metadata": {},
   "source": [
    "# Análisis de clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b55c1b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1,11):\n",
    "    km = KMeans(n_clusters=i,init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    km.fit(X)\n",
    "    wcss.append(km.inertia_)\n",
    "plt.plot(range(1,11),wcss, c=\"#c51b7d\")\n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.title('Métdo del codo (Elbow Method)', size=14)\n",
    "plt.xlabel('Número de clústeres', size=12)\n",
    "plt.ylabel('wcss', size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7f2fde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kmeans algorithm\n",
    "# n_clusters: Number of clusters. In our case 5\n",
    "# init: k-means++. Smart initialization\n",
    "# max_iter: Maximum number of iterations of the k-means algorithm for a single run\n",
    "# n_init: Number of time the k-means algorithm will be run with different centroid seeds. \n",
    "# random_state: Determines random number generation for centroid initialization.\n",
    "kmeans = KMeans(n_clusters=5, init='k-means++', max_iter=10, n_init=10, random_state=0)\n",
    "\n",
    "# Fit and predict \n",
    "y_means = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7056c1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "\n",
    "plt.scatter(pca_2d[:, 0], pca_2d[:, 1],\n",
    "            c=y_means, \n",
    "            edgecolor=\"none\", \n",
    "            cmap=plt.cm.get_cmap(\"Spectral_r\", 5),\n",
    "            alpha=0.5)\n",
    "        \n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.gca().spines[\"bottom\"].set_visible(False)\n",
    "plt.gca().spines[\"left\"].set_visible(False)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "plt.xlabel(\"Componente 1\", size = 14, labelpad=10)\n",
    "plt.ylabel(\"Componente 2\", size = 14, labelpad=10)\n",
    "\n",
    "plt.title('Dominios agrupados en 5 clusters', size=16)\n",
    "\n",
    "\n",
    "plt.colorbar(ticks=[0, 1, 2, 3, 4]);\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4a018b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = pd.DataFrame(kmeans.cluster_centers_, columns = [\"Age\", \"Annual Income\", \"Spending\", \"Male\", \"Female\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ea07402e",
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids.index_name = \"ClusterID\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4902c4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids[\"ClusterID\"] = centroids.index\n",
    "centroids = centroids.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ceedd8d",
   "metadata": {},
   "source": [
    "### Si la cantidad de datos es muy grande, k-means consumirá mucho tiempo de cómputo; pero es posible diseñar estrategias de muestreo para encontrar centroides con pocos datos y clasificar los históricos y los nuevos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a407946f",
   "metadata": {},
   "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>Age</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>ClusterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45.217391</td>\n",
       "      <td>26.304348</td>\n",
       "      <td>20.913043</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.692308</td>\n",
       "      <td>86.538462</td>\n",
       "      <td>82.128205</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.088608</td>\n",
       "      <td>55.291139</td>\n",
       "      <td>49.569620</td>\n",
       "      <td>0.582278</td>\n",
       "      <td>0.417722</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.666667</td>\n",
       "      <td>87.750000</td>\n",
       "      <td>17.583333</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.527778</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.521739</td>\n",
       "      <td>26.304348</td>\n",
       "      <td>78.565217</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  Annual Income   Spending      Male    Female  ClusterID\n",
       "0  45.217391      26.304348  20.913043  0.608696  0.391304          0\n",
       "1  32.692308      86.538462  82.128205  0.538462  0.461538          1\n",
       "2  43.088608      55.291139  49.569620  0.582278  0.417722          2\n",
       "3  40.666667      87.750000  17.583333  0.472222  0.527778          3\n",
       "4  25.521739      26.304348  78.565217  0.608696  0.391304          4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a11e9493",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-4888d606f09a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m48\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m76\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnew_customer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"El nuevo cliente pertecene al segmento {new_customer[0]}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[48, 76, 20, 0, 1]]) \n",
    " \n",
    "new_customer = kmeans.predict(X_new)\n",
    "print(f\"El nuevo cliente pertecene al segmento {new_customer[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370f6f1f",
   "metadata": {},
   "source": [
    "# II. Perfilado de cursos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f645189",
   "metadata": {},
   "source": [
    "### Comercio electrónico - Modificado de V. Patil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cdc7046d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42b369a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score as ss\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e906bfb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72d03b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_null_count(df):\n",
    "    df_null = pd.DataFrame(data=df.isnull().sum(),columns=['nulls'])\n",
    "    df_null = df_null.sort_values('nulls', ascending=False)\n",
    "    return df_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dc6b1d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = pd.read_csv('./data/clustering_sales.csv')\n",
    "df_customer = pd.read_csv('./data/clustering_customer.csv')\n",
    "df_product = pd.read_csv('./data/clustering_product.csv')\n",
    "df_payment = pd.read_csv('./data/clustering_payment.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5b9c5f6b",
   "metadata": {},
   "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>order_item_id</th>\n",
       "      <th>tran_dt</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>dollars</th>\n",
       "      <th>qty</th>\n",
       "      <th>product_id</th>\n",
       "      <th>payment_type_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>572</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>532</td>\n",
       "      <td>630</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>608</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>424</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>584</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  order_item_id     tran_dt  customer_id  dollars  qty  product_id  \\\n",
       "0         1              1  2020-01-01          572      550    1          20   \n",
       "1         2              2  2020-01-01          532      630    3          11   \n",
       "2         3              3  2020-01-01          608      450    2          18   \n",
       "3         4              4  2020-01-01          424      110    2          10   \n",
       "4         5              5  2020-01-01          584      250    1           8   \n",
       "\n",
       "   payment_type_id  \n",
       "0                2  \n",
       "1                2  \n",
       "2                4  \n",
       "3                2  \n",
       "4                4  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987e19dd",
   "metadata": {},
   "source": [
    "### Los datos están organizados por orden, por lo que se encontrarán muchos cursos (cursos_id). Es necesario convertir las entradas de eventos a un dataset de partes que pueda ser usado para un clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cbac585e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 8), 10000, 9811)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_sales.shape, df_sales.order_item_id.nunique(), df_sales.order_id.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9b9327d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   order_id         10000 non-null  int64 \n",
      " 1   order_item_id    10000 non-null  int64 \n",
      " 2   tran_dt          10000 non-null  object\n",
      " 3   customer_id      10000 non-null  int64 \n",
      " 4   dollars          10000 non-null  int64 \n",
      " 5   qty              10000 non-null  int64 \n",
      " 6   product_id       10000 non-null  int64 \n",
      " 7   payment_type_id  10000 non-null  int64 \n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 625.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b9651fdf",
   "metadata": {},
   "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>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>hh_income</th>\n",
       "      <th>omni_shopper</th>\n",
       "      <th>email_subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>640000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>890000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>772000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>303000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>412000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  age  hh_income  omni_shopper  email_subscribed\n",
       "0            1   46     640000             0                 0\n",
       "1            2   32     890000             1                 1\n",
       "2            3   45     772000             0                 0\n",
       "3            4   46     303000             0                 1\n",
       "4            5   38     412000             0                 0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "153151ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, (1000, 5), 1000)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_sales.customer_id.nunique(), df_customer.shape, df_customer.customer_id.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6cd4d486",
   "metadata": {},
   "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>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>E</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id category  price\n",
       "0           1        A    450\n",
       "1           2        B     80\n",
       "2           3        C    250\n",
       "3           4        D    400\n",
       "4           5        E     50"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_product.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "74029cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22, 3), 22)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_product.shape, df_product.product_id.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1849aca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payment_type_id</th>\n",
       "      <th>payment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>credit card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>debit card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>gift card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   payment_type_id payment_type\n",
       "0                1         cash\n",
       "1                2  credit card\n",
       "2                3   debit card\n",
       "3                4    gift card\n",
       "4                5       others"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_payment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38b967a",
   "metadata": {},
   "source": [
    "Los datos con los que se cuenta son de una año calendario para el 2020 y conformarán en su totalidad, un timeFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eb09cee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = df_sales.merge(df_product[['product_id','category']], on=['product_id'])\n",
    "df_sales = df_sales.merge(df_payment, on=['payment_type_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "88b518ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features_overall = df_sales.groupby(['customer_id']).agg({\n",
    "    'dollars':'sum',\n",
    "    'qty':'sum',\n",
    "    'order_id':'nunique',\n",
    "    'product_id':'nunique',\n",
    "    'payment_type_id':'nunique',\n",
    "    'category':'nunique'\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8d2f7fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features_overall['aov'] = df_features_overall['dollars']/df_features_overall['order_id']\n",
    "df_features_overall['aur'] = df_features_overall['dollars']/df_features_overall['qty']\n",
    "df_features_overall['upt'] = df_features_overall['qty']/df_features_overall['order_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "001a395c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features_overall.columns = [\n",
    "    'sales','units','orders','unique_products_bought','unique_payments_used',\n",
    "    'unique_categories_bought','aov','aur','upt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c0a2508d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\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></th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>orders</th>\n",
       "      <th>unique_products_bought</th>\n",
       "      <th>unique_payments_used</th>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <th>aov</th>\n",
       "      <th>aur</th>\n",
       "      <th>upt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_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>2395</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>217.727273</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4815</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>687.857143</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>2.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>428.500000</td>\n",
       "      <td>204.047619</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12000</td>\n",
       "      <td>44</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>571.428571</td>\n",
       "      <td>272.727273</td>\n",
       "      <td>2.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1700</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>212.500000</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  units  orders  unique_products_bought  \\\n",
       "customer_id                                                 \n",
       "1             2395     11       5                       5   \n",
       "2             4815     15       7                       6   \n",
       "3             4285     21      10                       9   \n",
       "4            12000     44      21                      15   \n",
       "5             1700      8       3                       3   \n",
       "\n",
       "             unique_payments_used  unique_categories_bought         aov  \\\n",
       "customer_id                                                               \n",
       "1                               3                         3  479.000000   \n",
       "2                               4                         3  687.857143   \n",
       "3                               4                         5  428.500000   \n",
       "4                               4                         5  571.428571   \n",
       "5                               2                         2  566.666667   \n",
       "\n",
       "                    aur       upt  \n",
       "customer_id                        \n",
       "1            217.727273  2.200000  \n",
       "2            321.000000  2.142857  \n",
       "3            204.047619  2.100000  \n",
       "4            272.727273  2.095238  \n",
       "5            212.500000  2.666667  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_overall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921d0c80",
   "metadata": {},
   "source": [
    "## Métricas para comercio electrónico: tasa de conversión, valor promedio de las órdenes (aov = total ingresado/número de chackouts), ... y unidates promedio por ticket (upt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2169b6ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_s = df_sales.groupby(['customer_id','category']).agg({'dollars':'sum'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "81213a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_s = df_category_features_s.merge(df_features_overall[['sales']], on=['customer_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ebf1fa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_s['sales_perc'] = df_category_features_s['dollars']/df_category_features_s['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8afbf91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>category</th>\n",
       "      <th>dollars</th>\n",
       "      <th>sales</th>\n",
       "      <th>sales_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1150</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.480167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1080</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.450939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>165</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.068894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>3475</td>\n",
       "      <td>4815</td>\n",
       "      <td>0.721703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>1190</td>\n",
       "      <td>4815</td>\n",
       "      <td>0.247144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id category  dollars  sales  sales_perc\n",
       "0            1        A     1150   2395    0.480167\n",
       "1            1        C     1080   2395    0.450939\n",
       "2            1        E      165   2395    0.068894\n",
       "3            2        A     3475   4815    0.721703\n",
       "4            2        C     1190   4815    0.247144"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_category_features_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "88557b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_s = df_category_features_s.pivot(index='customer_id', columns='category', values='sales_perc')\n",
    "df_category_features_s.columns = [\n",
    "    'category_a_sales','category_b_sales','category_c_sales','category_d_sales','category_e_sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "866aec33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_a_sales</th>\n",
       "      <th>category_b_sales</th>\n",
       "      <th>category_c_sales</th>\n",
       "      <th>category_d_sales</th>\n",
       "      <th>category_e_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.450939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.721703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.247144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.240373</td>\n",
       "      <td>0.175029</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.210035</td>\n",
       "      <td>0.065344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.028333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             category_a_sales  category_b_sales  category_c_sales  \\\n",
       "customer_id                                                         \n",
       "1                    0.480167               NaN          0.450939   \n",
       "2                    0.721703               NaN          0.247144   \n",
       "3                    0.240373          0.175029          0.309218   \n",
       "4                    0.575000          0.067500          0.158333   \n",
       "5                         NaN          0.264706          0.735294   \n",
       "\n",
       "             category_d_sales  category_e_sales  \n",
       "customer_id                                      \n",
       "1                         NaN          0.068894  \n",
       "2                         NaN          0.031153  \n",
       "3                    0.210035          0.065344  \n",
       "4                    0.170833          0.028333  \n",
       "5                         NaN               NaN  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_category_features_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fa9a6599",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_u = df_sales.groupby(['customer_id','category']).agg({'qty':'sum'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ed513ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_u = df_category_features_u.merge(df_features_overall[['units']], on=['customer_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1bd59048",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_u['units_perc'] = df_category_features_u['qty']/df_category_features_u['units']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3be202fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>category</th>\n",
       "      <th>qty</th>\n",
       "      <th>units</th>\n",
       "      <th>units_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id category  qty  units  units_perc\n",
       "0            1        A    4     11    0.363636\n",
       "1            1        C    4     11    0.363636\n",
       "2            1        E    3     11    0.272727\n",
       "3            2        A    7     15    0.466667\n",
       "4            2        C    5     15    0.333333"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_category_features_u.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "924217f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category_features_u = df_category_features_u.pivot(index='customer_id', columns='category', values='units_perc')\n",
    "df_category_features_u.columns = [\n",
    "    'category_a_units','category_b_units','category_c_units','category_d_units','category_e_units']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ebc6eb88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>category_a_units</th>\n",
       "      <th>category_b_units</th>\n",
       "      <th>category_c_units</th>\n",
       "      <th>category_d_units</th>\n",
       "      <th>category_e_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             category_a_units  category_b_units  category_c_units  \\\n",
       "customer_id                                                         \n",
       "1                    0.363636               NaN          0.363636   \n",
       "2                    0.466667               NaN          0.333333   \n",
       "3                    0.190476          0.238095          0.238095   \n",
       "4                    0.409091          0.159091          0.181818   \n",
       "5                         NaN          0.375000          0.625000   \n",
       "\n",
       "             category_d_units  category_e_units  \n",
       "customer_id                                      \n",
       "1                         NaN          0.272727  \n",
       "2                         NaN          0.200000  \n",
       "3                    0.095238          0.238095  \n",
       "4                    0.113636          0.136364  \n",
       "5                         NaN               NaN  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_category_features_u.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d0266237",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_payment_features = df_sales.groupby(['customer_id','payment_type']).agg({'dollars':'sum'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "db606f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_payment_features = df_payment_features.merge(df_features_overall[['sales']], on=['customer_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "04d4cba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_payment_features['sales_perc'] = df_payment_features['dollars']/df_payment_features['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bbc989fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>customer_id</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>dollars</th>\n",
       "      <th>sales</th>\n",
       "      <th>sales_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>credit card</td>\n",
       "      <td>1245</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.519833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>debit card</td>\n",
       "      <td>400</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.167015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>gift card</td>\n",
       "      <td>750</td>\n",
       "      <td>2395</td>\n",
       "      <td>0.313152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>150</td>\n",
       "      <td>4815</td>\n",
       "      <td>0.031153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>credit card</td>\n",
       "      <td>3390</td>\n",
       "      <td>4815</td>\n",
       "      <td>0.704050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id payment_type  dollars  sales  sales_perc\n",
       "0            1  credit card     1245   2395    0.519833\n",
       "1            1   debit card      400   2395    0.167015\n",
       "2            1    gift card      750   2395    0.313152\n",
       "3            2         cash      150   4815    0.031153\n",
       "4            2  credit card     3390   4815    0.704050"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_payment_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d4010860",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_payment_features = df_payment_features.pivot(index='customer_id', columns='payment_type', values='sales_perc')\n",
    "df_payment_features.columns = [\n",
    "    'payment_cash','payment_credit','payment_debit','payment_gc','payment_others']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9c6050d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payment_cash</th>\n",
       "      <th>payment_credit</th>\n",
       "      <th>payment_debit</th>\n",
       "      <th>payment_gc</th>\n",
       "      <th>payment_others</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>NaN</td>\n",
       "      <td>0.519833</td>\n",
       "      <td>0.167015</td>\n",
       "      <td>0.313152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.704050</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.077882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.263711</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.147025</td>\n",
       "      <td>0.280047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             payment_cash  payment_credit  payment_debit  payment_gc  \\\n",
       "customer_id                                                            \n",
       "1                     NaN        0.519833       0.167015    0.313152   \n",
       "2                0.031153        0.704050       0.186916    0.077882   \n",
       "3                     NaN        0.263711       0.309218    0.147025   \n",
       "4                0.066667        0.345000       0.370000    0.218333   \n",
       "5                     NaN             NaN       0.735294    0.264706   \n",
       "\n",
       "             payment_others  \n",
       "customer_id                  \n",
       "1                       NaN  \n",
       "2                       NaN  \n",
       "3                  0.280047  \n",
       "4                       NaN  \n",
       "5                       NaN  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_payment_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "bca008d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_features_overall.merge(df_category_features_s,on='customer_id',how='left')\n",
    "\n",
    "df_features = df_features.merge(df_category_features_u,on='customer_id',how='left')\n",
    "\n",
    "df_features = df_features.merge(df_payment_features,on='customer_id',how='left')\n",
    "\n",
    "df_features = df_features.merge(\n",
    "    df_customer[['customer_id','email_subscribed','omni_shopper']].set_index('customer_id'),\n",
    "    on='customer_id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "71c5f8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>orders</th>\n",
       "      <th>unique_products_bought</th>\n",
       "      <th>unique_payments_used</th>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <th>aov</th>\n",
       "      <th>aur</th>\n",
       "      <th>upt</th>\n",
       "      <th>category_a_sales</th>\n",
       "      <th>category_b_sales</th>\n",
       "      <th>category_c_sales</th>\n",
       "      <th>category_d_sales</th>\n",
       "      <th>category_e_sales</th>\n",
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       "      <th>category_b_units</th>\n",
       "      <th>category_c_units</th>\n",
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       "      <th>payment_cash</th>\n",
       "      <th>payment_credit</th>\n",
       "      <th>payment_debit</th>\n",
       "      <th>payment_gc</th>\n",
       "      <th>payment_others</th>\n",
       "      <th>email_subscribed</th>\n",
       "      <th>omni_shopper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2395</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>217.727273</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.480167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068894</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.519833</td>\n",
       "      <td>0.167015</td>\n",
       "      <td>0.313152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4815</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>687.857143</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>0.721703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.247144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.704050</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.077882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>428.500000</td>\n",
       "      <td>204.047619</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.240373</td>\n",
       "      <td>0.175029</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.210035</td>\n",
       "      <td>0.065344</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.263711</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.147025</td>\n",
       "      <td>0.280047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12000</td>\n",
       "      <td>44</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>571.428571</td>\n",
       "      <td>272.727273</td>\n",
       "      <td>2.095238</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.028333</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1700</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>212.500000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  units  orders  unique_products_bought  \\\n",
       "customer_id                                                 \n",
       "1             2395     11       5                       5   \n",
       "2             4815     15       7                       6   \n",
       "3             4285     21      10                       9   \n",
       "4            12000     44      21                      15   \n",
       "5             1700      8       3                       3   \n",
       "\n",
       "             unique_payments_used  unique_categories_bought         aov  \\\n",
       "customer_id                                                               \n",
       "1                               3                         3  479.000000   \n",
       "2                               4                         3  687.857143   \n",
       "3                               4                         5  428.500000   \n",
       "4                               4                         5  571.428571   \n",
       "5                               2                         2  566.666667   \n",
       "\n",
       "                    aur       upt  category_a_sales  category_b_sales  \\\n",
       "customer_id                                                             \n",
       "1            217.727273  2.200000          0.480167               NaN   \n",
       "2            321.000000  2.142857          0.721703               NaN   \n",
       "3            204.047619  2.100000          0.240373          0.175029   \n",
       "4            272.727273  2.095238          0.575000          0.067500   \n",
       "5            212.500000  2.666667               NaN          0.264706   \n",
       "\n",
       "             category_c_sales  category_d_sales  category_e_sales  \\\n",
       "customer_id                                                         \n",
       "1                    0.450939               NaN          0.068894   \n",
       "2                    0.247144               NaN          0.031153   \n",
       "3                    0.309218          0.210035          0.065344   \n",
       "4                    0.158333          0.170833          0.028333   \n",
       "5                    0.735294               NaN               NaN   \n",
       "\n",
       "             category_a_units  category_b_units  category_c_units  \\\n",
       "customer_id                                                         \n",
       "1                    0.363636               NaN          0.363636   \n",
       "2                    0.466667               NaN          0.333333   \n",
       "3                    0.190476          0.238095          0.238095   \n",
       "4                    0.409091          0.159091          0.181818   \n",
       "5                         NaN          0.375000          0.625000   \n",
       "\n",
       "             category_d_units  category_e_units  payment_cash  payment_credit  \\\n",
       "customer_id                                                                     \n",
       "1                         NaN          0.272727           NaN        0.519833   \n",
       "2                         NaN          0.200000      0.031153        0.704050   \n",
       "3                    0.095238          0.238095           NaN        0.263711   \n",
       "4                    0.113636          0.136364      0.066667        0.345000   \n",
       "5                         NaN               NaN           NaN             NaN   \n",
       "\n",
       "             payment_debit  payment_gc  payment_others  email_subscribed  \\\n",
       "customer_id                                                                \n",
       "1                 0.167015    0.313152             NaN                 0   \n",
       "2                 0.186916    0.077882             NaN                 1   \n",
       "3                 0.309218    0.147025        0.280047                 0   \n",
       "4                 0.370000    0.218333             NaN                 1   \n",
       "5                 0.735294    0.264706             NaN                 0   \n",
       "\n",
       "             omni_shopper  \n",
       "customer_id                \n",
       "1                       0  \n",
       "2                       1  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c46e901f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.to_csv('./data/clustering_features.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c2cbfd44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>5707.170000</td>\n",
       "      <td>4036.079859</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>2827.500000</td>\n",
       "      <td>4280.000000</td>\n",
       "      <td>7267.500000</td>\n",
       "      <td>17635.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>14.408398</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>9.811000</td>\n",
       "      <td>6.384986</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_products_bought</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>7.547000</td>\n",
       "      <td>3.850535</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_payments_used</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.005000</td>\n",
       "      <td>0.948617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.843000</td>\n",
       "      <td>1.014584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aov</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>575.336384</td>\n",
       "      <td>165.741267</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>467.500000</td>\n",
       "      <td>568.444444</td>\n",
       "      <td>670.238095</td>\n",
       "      <td>1202.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aur</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>271.150530</td>\n",
       "      <td>65.941495</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>227.836538</td>\n",
       "      <td>268.178138</td>\n",
       "      <td>311.271777</td>\n",
       "      <td>485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upt</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.124689</td>\n",
       "      <td>0.344007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.139610</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_sales</th>\n",
       "      <td>911.0</td>\n",
       "      <td>0.465008</td>\n",
       "      <td>0.200729</td>\n",
       "      <td>0.040698</td>\n",
       "      <td>0.316973</td>\n",
       "      <td>0.459397</td>\n",
       "      <td>0.598472</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_sales</th>\n",
       "      <td>754.0</td>\n",
       "      <td>0.129013</td>\n",
       "      <td>0.109362</td>\n",
       "      <td>0.005324</td>\n",
       "      <td>0.059289</td>\n",
       "      <td>0.103094</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_sales</th>\n",
       "      <td>829.0</td>\n",
       "      <td>0.275343</td>\n",
       "      <td>0.169208</td>\n",
       "      <td>0.021024</td>\n",
       "      <td>0.154321</td>\n",
       "      <td>0.244739</td>\n",
       "      <td>0.352052</td>\n",
       "      <td>0.951860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_sales</th>\n",
       "      <td>678.0</td>\n",
       "      <td>0.317048</td>\n",
       "      <td>0.173845</td>\n",
       "      <td>0.033003</td>\n",
       "      <td>0.183344</td>\n",
       "      <td>0.281823</td>\n",
       "      <td>0.422400</td>\n",
       "      <td>0.940171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_sales</th>\n",
       "      <td>671.0</td>\n",
       "      <td>0.053478</td>\n",
       "      <td>0.064436</td>\n",
       "      <td>0.003299</td>\n",
       "      <td>0.022009</td>\n",
       "      <td>0.037415</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_units</th>\n",
       "      <td>911.0</td>\n",
       "      <td>0.351488</td>\n",
       "      <td>0.173091</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.226415</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_units</th>\n",
       "      <td>754.0</td>\n",
       "      <td>0.241836</td>\n",
       "      <td>0.134064</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.218452</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_units</th>\n",
       "      <td>829.0</td>\n",
       "      <td>0.271817</td>\n",
       "      <td>0.149078</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_units</th>\n",
       "      <td>678.0</td>\n",
       "      <td>0.200435</td>\n",
       "      <td>0.126031</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.168699</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_units</th>\n",
       "      <td>671.0</td>\n",
       "      <td>0.203009</td>\n",
       "      <td>0.131912</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_cash</th>\n",
       "      <td>263.0</td>\n",
       "      <td>0.128239</td>\n",
       "      <td>0.126068</td>\n",
       "      <td>0.003754</td>\n",
       "      <td>0.038977</td>\n",
       "      <td>0.095969</td>\n",
       "      <td>0.168276</td>\n",
       "      <td>0.947867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_credit</th>\n",
       "      <td>981.0</td>\n",
       "      <td>0.552566</td>\n",
       "      <td>0.211687</td>\n",
       "      <td>0.031339</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.544426</td>\n",
       "      <td>0.702323</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_debit</th>\n",
       "      <td>871.0</td>\n",
       "      <td>0.307976</td>\n",
       "      <td>0.183177</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>0.172142</td>\n",
       "      <td>0.277212</td>\n",
       "      <td>0.406788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_gc</th>\n",
       "      <td>634.0</td>\n",
       "      <td>0.202083</td>\n",
       "      <td>0.160023</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.081417</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>0.275592</td>\n",
       "      <td>0.968661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_others</th>\n",
       "      <td>256.0</td>\n",
       "      <td>0.108743</td>\n",
       "      <td>0.102174</td>\n",
       "      <td>0.003154</td>\n",
       "      <td>0.041317</td>\n",
       "      <td>0.080988</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>0.605042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email_subscribed</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.484622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omni_shopper</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>0.432650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count         mean          std         min  \\\n",
       "sales                     1000.0  5707.170000  4036.079859  330.000000   \n",
       "units                     1000.0    21.108000    14.408398    3.000000   \n",
       "orders                    1000.0     9.811000     6.384986    3.000000   \n",
       "unique_products_bought    1000.0     7.547000     3.850535    2.000000   \n",
       "unique_payments_used      1000.0     3.005000     0.948617    1.000000   \n",
       "unique_categories_bought  1000.0     3.843000     1.014584    1.000000   \n",
       "aov                       1000.0   575.336384   165.741267  110.000000   \n",
       "aur                       1000.0   271.150530    65.941495   60.000000   \n",
       "upt                       1000.0     2.124689     0.344007    1.000000   \n",
       "category_a_sales           911.0     0.465008     0.200729    0.040698   \n",
       "category_b_sales           754.0     0.129013     0.109362    0.005324   \n",
       "category_c_sales           829.0     0.275343     0.169208    0.021024   \n",
       "category_d_sales           678.0     0.317048     0.173845    0.033003   \n",
       "category_e_sales           671.0     0.053478     0.064436    0.003299   \n",
       "category_a_units           911.0     0.351488     0.173091    0.043478   \n",
       "category_b_units           754.0     0.241836     0.134064    0.023256   \n",
       "category_c_units           829.0     0.271817     0.149078    0.022727   \n",
       "category_d_units           678.0     0.200435     0.126031    0.019608   \n",
       "category_e_units           671.0     0.203009     0.131912    0.019608   \n",
       "payment_cash               263.0     0.128239     0.126068    0.003754   \n",
       "payment_credit             981.0     0.552566     0.211687    0.031339   \n",
       "payment_debit              871.0     0.307976     0.183177    0.008929   \n",
       "payment_gc                 634.0     0.202083     0.160023    0.007018   \n",
       "payment_others             256.0     0.108743     0.102174    0.003154   \n",
       "email_subscribed          1000.0     0.624000     0.484622    0.000000   \n",
       "omni_shopper              1000.0     0.249000     0.432650    0.000000   \n",
       "\n",
       "                                  25%          50%          75%           max  \n",
       "sales                     2827.500000  4280.000000  7267.500000  17635.000000  \n",
       "units                       11.000000    16.000000    23.000000     58.000000  \n",
       "orders                       5.000000     8.000000    10.000000     24.000000  \n",
       "unique_products_bought       5.000000     6.000000     9.000000     18.000000  \n",
       "unique_payments_used         2.000000     3.000000     4.000000      5.000000  \n",
       "unique_categories_bought     3.000000     4.000000     5.000000      5.000000  \n",
       "aov                        467.500000   568.444444   670.238095   1202.500000  \n",
       "aur                        227.836538   268.178138   311.271777    485.000000  \n",
       "upt                          1.900000     2.139610     2.333333      3.666667  \n",
       "category_a_sales             0.316973     0.459397     0.598472      1.000000  \n",
       "category_b_sales             0.059289     0.103094     0.166667      1.000000  \n",
       "category_c_sales             0.154321     0.244739     0.352052      0.951860  \n",
       "category_d_sales             0.183344     0.281823     0.422400      0.940171  \n",
       "category_e_sales             0.022009     0.037415     0.062500      0.809524  \n",
       "category_a_units             0.226415     0.333333     0.454545      1.000000  \n",
       "category_b_units             0.142857     0.218452     0.312500      1.000000  \n",
       "category_c_units             0.166667     0.250000     0.333333      0.857143  \n",
       "category_d_units             0.105263     0.168699     0.266667      0.727273  \n",
       "category_e_units             0.111111     0.166667     0.263158      0.888889  \n",
       "payment_cash                 0.038977     0.095969     0.168276      0.947867  \n",
       "payment_credit               0.411765     0.544426     0.702323      1.000000  \n",
       "payment_debit                0.172142     0.277212     0.406788      1.000000  \n",
       "payment_gc                   0.081417     0.157143     0.275592      0.968661  \n",
       "payment_others               0.041317     0.080988     0.144144      0.605042  \n",
       "email_subscribed             0.000000     1.000000     1.000000      1.000000  \n",
       "omni_shopper                 0.000000     0.000000     0.000000      1.000000  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e6adc169",
   "metadata": {},
   "outputs": [
    {
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       "      <th>nulls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>payment_others</th>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_cash</th>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_gc</th>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_sales</th>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_units</th>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_sales</th>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_units</th>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_sales</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_units</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_units</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_sales</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_debit</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_sales</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_units</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_credit</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email_subscribed</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aur</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aov</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_payments_used</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_products_bought</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omni_shopper</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          nulls\n",
       "payment_others              744\n",
       "payment_cash                737\n",
       "payment_gc                  366\n",
       "category_e_sales            329\n",
       "category_e_units            329\n",
       "category_d_sales            322\n",
       "category_d_units            322\n",
       "category_b_sales            246\n",
       "category_b_units            246\n",
       "category_c_units            171\n",
       "category_c_sales            171\n",
       "payment_debit               129\n",
       "category_a_sales             89\n",
       "category_a_units             89\n",
       "payment_credit               19\n",
       "email_subscribed              0\n",
       "sales                         0\n",
       "units                         0\n",
       "upt                           0\n",
       "aur                           0\n",
       "aov                           0\n",
       "unique_categories_bought      0\n",
       "unique_payments_used          0\n",
       "unique_products_bought        0\n",
       "orders                        0\n",
       "omni_shopper                  0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_null_count(df_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5dacb2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_features.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8c224eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cb862e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clust = df_features.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e528261d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_scale = [\n",
    "    'sales','units','upt','aur','aov','unique_categories_bought','unique_payments_used',\n",
    "    'unique_products_bought','orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f816461d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 26)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>orders</th>\n",
       "      <th>unique_products_bought</th>\n",
       "      <th>unique_payments_used</th>\n",
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       "      <th>payment_cash</th>\n",
       "      <th>payment_credit</th>\n",
       "      <th>payment_debit</th>\n",
       "      <th>payment_gc</th>\n",
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       "      <th>email_subscribed</th>\n",
       "      <th>omni_shopper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.119330</td>\n",
       "      <td>0.145455</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.337757</td>\n",
       "      <td>0.371123</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.480167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068894</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.519833</td>\n",
       "      <td>0.167015</td>\n",
       "      <td>0.313152</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.259174</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.528931</td>\n",
       "      <td>0.614118</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.721703</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.247144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.704050</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.077882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.228547</td>\n",
       "      <td>0.327273</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.291533</td>\n",
       "      <td>0.338936</td>\n",
       "      <td>0.412500</td>\n",
       "      <td>0.240373</td>\n",
       "      <td>0.175029</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.210035</td>\n",
       "      <td>0.065344</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.263711</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.147025</td>\n",
       "      <td>0.280047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.674372</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.422360</td>\n",
       "      <td>0.500535</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.028333</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.079168</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.418002</td>\n",
       "      <td>0.358824</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                sales     units    orders  unique_products_bought  \\\n",
       "customer_id                                                         \n",
       "1            0.119330  0.145455  0.095238                  0.1875   \n",
       "2            0.259174  0.218182  0.190476                  0.2500   \n",
       "3            0.228547  0.327273  0.333333                  0.4375   \n",
       "4            0.674372  0.745455  0.857143                  0.8125   \n",
       "5            0.079168  0.090909  0.000000                  0.0625   \n",
       "\n",
       "             unique_payments_used  unique_categories_bought       aov  \\\n",
       "customer_id                                                             \n",
       "1                            0.50                      0.50  0.337757   \n",
       "2                            0.75                      0.50  0.528931   \n",
       "3                            0.75                      1.00  0.291533   \n",
       "4                            0.75                      1.00  0.422360   \n",
       "5                            0.25                      0.25  0.418002   \n",
       "\n",
       "                  aur       upt  category_a_sales  category_b_sales  \\\n",
       "customer_id                                                           \n",
       "1            0.371123  0.450000          0.480167          0.000000   \n",
       "2            0.614118  0.428571          0.721703          0.000000   \n",
       "3            0.338936  0.412500          0.240373          0.175029   \n",
       "4            0.500535  0.410714          0.575000          0.067500   \n",
       "5            0.358824  0.625000          0.000000          0.264706   \n",
       "\n",
       "             category_c_sales  category_d_sales  category_e_sales  \\\n",
       "customer_id                                                         \n",
       "1                    0.450939          0.000000          0.068894   \n",
       "2                    0.247144          0.000000          0.031153   \n",
       "3                    0.309218          0.210035          0.065344   \n",
       "4                    0.158333          0.170833          0.028333   \n",
       "5                    0.735294          0.000000          0.000000   \n",
       "\n",
       "             category_a_units  category_b_units  category_c_units  \\\n",
       "customer_id                                                         \n",
       "1                    0.363636          0.000000          0.363636   \n",
       "2                    0.466667          0.000000          0.333333   \n",
       "3                    0.190476          0.238095          0.238095   \n",
       "4                    0.409091          0.159091          0.181818   \n",
       "5                    0.000000          0.375000          0.625000   \n",
       "\n",
       "             category_d_units  category_e_units  payment_cash  payment_credit  \\\n",
       "customer_id                                                                     \n",
       "1                    0.000000          0.272727      0.000000        0.519833   \n",
       "2                    0.000000          0.200000      0.031153        0.704050   \n",
       "3                    0.095238          0.238095      0.000000        0.263711   \n",
       "4                    0.113636          0.136364      0.066667        0.345000   \n",
       "5                    0.000000          0.000000      0.000000        0.000000   \n",
       "\n",
       "             payment_debit  payment_gc  payment_others  email_subscribed  \\\n",
       "customer_id                                                                \n",
       "1                 0.167015    0.313152        0.000000                 0   \n",
       "2                 0.186916    0.077882        0.000000                 1   \n",
       "3                 0.309218    0.147025        0.280047                 0   \n",
       "4                 0.370000    0.218333        0.000000                 1   \n",
       "5                 0.735294    0.264706        0.000000                 0   \n",
       "\n",
       "             omni_shopper  \n",
       "customer_id                \n",
       "1                       0  \n",
       "2                       1  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler().fit(df_clust[cols_scale])\n",
    "df_clust[cols_scale] = scaler.transform(df_clust[cols_scale])\n",
    "\n",
    "print(df_clust.shape)\n",
    "df_clust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "63ca9f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_filename = \"./files/model_objects/kmeans_scaler_model.pkl\"\n",
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(scaler, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f1c9976c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orders</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.324333</td>\n",
       "      <td>0.304047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_products_bought</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.346687</td>\n",
       "      <td>0.240658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_payments_used</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.501250</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.710750</td>\n",
       "      <td>0.253646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aov</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.425937</td>\n",
       "      <td>0.151708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.327231</td>\n",
       "      <td>0.419629</td>\n",
       "      <td>0.512804</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aur</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.496825</td>\n",
       "      <td>0.155156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.394910</td>\n",
       "      <td>0.489831</td>\n",
       "      <td>0.591228</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upt</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.421759</td>\n",
       "      <td>0.129003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.337500</td>\n",
       "      <td>0.427354</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.423622</td>\n",
       "      <td>0.232920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.270173</td>\n",
       "      <td>0.428711</td>\n",
       "      <td>0.585021</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.097276</td>\n",
       "      <td>0.110024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015163</td>\n",
       "      <td>0.074244</td>\n",
       "      <td>0.140781</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.228259</td>\n",
       "      <td>0.185711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092650</td>\n",
       "      <td>0.205285</td>\n",
       "      <td>0.326688</td>\n",
       "      <td>0.951860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.214959</td>\n",
       "      <td>0.206029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188518</td>\n",
       "      <td>0.350588</td>\n",
       "      <td>0.940171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_sales</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.035884</td>\n",
       "      <td>0.058451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.046804</td>\n",
       "      <td>0.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_a_units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.320206</td>\n",
       "      <td>0.193179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.310728</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_b_units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.182344</td>\n",
       "      <td>0.156225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044960</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_c_units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.225336</td>\n",
       "      <td>0.170013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.207940</td>\n",
       "      <td>0.326629</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_d_units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.135895</td>\n",
       "      <td>0.139798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106763</td>\n",
       "      <td>0.208576</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_e_units</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.136219</td>\n",
       "      <td>0.144143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.112374</td>\n",
       "      <td>0.208576</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_cash</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.033727</td>\n",
       "      <td>0.085784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010809</td>\n",
       "      <td>0.947867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_credit</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.542067</td>\n",
       "      <td>0.222836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.403791</td>\n",
       "      <td>0.539482</td>\n",
       "      <td>0.693278</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_debit</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.268247</td>\n",
       "      <td>0.199722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123380</td>\n",
       "      <td>0.247137</td>\n",
       "      <td>0.383302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_gc</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.128121</td>\n",
       "      <td>0.160347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072838</td>\n",
       "      <td>0.198798</td>\n",
       "      <td>0.968661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_others</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.027838</td>\n",
       "      <td>0.070137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.605042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email_subscribed</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.484622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omni_shopper</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>0.432650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count      mean       std  min       25%       50%  \\\n",
       "sales                     1000.0  0.310729  0.233232  0.0  0.144322  0.228258   \n",
       "units                     1000.0  0.329236  0.261971  0.0  0.145455  0.236364   \n",
       "orders                    1000.0  0.324333  0.304047  0.0  0.095238  0.238095   \n",
       "unique_products_bought    1000.0  0.346687  0.240658  0.0  0.187500  0.250000   \n",
       "unique_payments_used      1000.0  0.501250  0.237154  0.0  0.250000  0.500000   \n",
       "unique_categories_bought  1000.0  0.710750  0.253646  0.0  0.500000  0.750000   \n",
       "aov                       1000.0  0.425937  0.151708  0.0  0.327231  0.419629   \n",
       "aur                       1000.0  0.496825  0.155156  0.0  0.394910  0.489831   \n",
       "upt                       1000.0  0.421759  0.129003  0.0  0.337500  0.427354   \n",
       "category_a_sales          1000.0  0.423622  0.232920  0.0  0.270173  0.428711   \n",
       "category_b_sales          1000.0  0.097276  0.110024  0.0  0.015163  0.074244   \n",
       "category_c_sales          1000.0  0.228259  0.185711  0.0  0.092650  0.205285   \n",
       "category_d_sales          1000.0  0.214959  0.206029  0.0  0.000000  0.188518   \n",
       "category_e_sales          1000.0  0.035884  0.058451  0.0  0.000000  0.022222   \n",
       "category_a_units          1000.0  0.320206  0.193179  0.0  0.187500  0.310728   \n",
       "category_b_units          1000.0  0.182344  0.156225  0.0  0.044960  0.166667   \n",
       "category_c_units          1000.0  0.225336  0.170013  0.0  0.100000  0.207940   \n",
       "category_d_units          1000.0  0.135895  0.139798  0.0  0.000000  0.106763   \n",
       "category_e_units          1000.0  0.136219  0.144143  0.0  0.000000  0.112374   \n",
       "payment_cash              1000.0  0.033727  0.085784  0.0  0.000000  0.000000   \n",
       "payment_credit            1000.0  0.542067  0.222836  0.0  0.403791  0.539482   \n",
       "payment_debit             1000.0  0.268247  0.199722  0.0  0.123380  0.247137   \n",
       "payment_gc                1000.0  0.128121  0.160347  0.0  0.000000  0.072838   \n",
       "payment_others            1000.0  0.027838  0.070137  0.0  0.000000  0.000000   \n",
       "email_subscribed          1000.0  0.624000  0.484622  0.0  0.000000  1.000000   \n",
       "omni_shopper              1000.0  0.249000  0.432650  0.0  0.000000  0.000000   \n",
       "\n",
       "                               75%       max  \n",
       "sales                     0.400896  1.000000  \n",
       "units                     0.363636  1.000000  \n",
       "orders                    0.333333  1.000000  \n",
       "unique_products_bought    0.437500  1.000000  \n",
       "unique_payments_used      0.750000  1.000000  \n",
       "unique_categories_bought  1.000000  1.000000  \n",
       "aov                       0.512804  1.000000  \n",
       "aur                       0.591228  1.000000  \n",
       "upt                       0.500000  1.000000  \n",
       "category_a_sales          0.585021  1.000000  \n",
       "category_b_sales          0.140781  1.000000  \n",
       "category_c_sales          0.326688  0.951860  \n",
       "category_d_sales          0.350588  0.940171  \n",
       "category_e_sales          0.046804  0.809524  \n",
       "category_a_units          0.437500  1.000000  \n",
       "category_b_units          0.277778  1.000000  \n",
       "category_c_units          0.326629  0.857143  \n",
       "category_d_units          0.208576  0.727273  \n",
       "category_e_units          0.208576  0.888889  \n",
       "payment_cash              0.010809  0.947867  \n",
       "payment_credit            0.693278  1.000000  \n",
       "payment_debit             0.383302  1.000000  \n",
       "payment_gc                0.198798  0.968661  \n",
       "payment_others            0.008834  0.605042  \n",
       "email_subscribed          1.000000  1.000000  \n",
       "omni_shopper              0.000000  1.000000  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clust.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "de9f01a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 19/19 [00:05<00:00,  3.31it/s]\n"
     ]
    }
   ],
   "source": [
    "k1 = []\n",
    "sscore1 = []\n",
    "inertia_s1 = []\n",
    "\n",
    "for i in tqdm(range(2,21)):\n",
    "    k1.append(i)\n",
    "    kmeans1 = KMeans(n_clusters=i,random_state=125,max_iter=100).fit(df_clust)\n",
    "    # print('k-means done')\n",
    "    sscore1.append(ss(df_clust,kmeans1.labels_))\n",
    "    # print('ss score done')\n",
    "    inertia_s1.append(kmeans1.inertia_)\n",
    "    # print('inertia done')\n",
    "    # print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "838d9473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Inertia score (SSE)')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(k1,inertia_s1,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Inertia (SSE) vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Inertia score (SSE)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5001dcde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SS')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(k1,sscore1,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('SS vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('SS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ba393da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sscore 0.2644425716291346\n",
      "inertia 591.0601380602852\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=6,random_state=125,max_iter=100).fit(df_clust)\n",
    "sscore = ss(df_clust,kmeans.labels_)\n",
    "inertia= kmeans.inertia_\n",
    "\n",
    "print('sscore',sscore)\n",
    "print('inertia',inertia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5d72e21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save k-means\n",
    "pkl_filename = \"./files/model_objects/kmeans_model.pkl\"\n",
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(kmeans, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "18bc6493",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features['cluster_ids'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e0238d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_features.merge(\n",
    "    df_customer[['customer_id','age','hh_income']].set_index('customer_id'),\n",
    "    on='customer_id',how='left')\n",
    "df_features = df_features.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d74e694e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_profile_overall = df_features.describe().T\n",
    "\n",
    "# use median for age and hh_income\n",
    "df_profile_overall['Overall Dataset'] = df_profile_overall.apply(\n",
    "    lambda row: row['50%']   if row.name in ['age','hh_income'] else\n",
    "    row['count'] if row.name in ['customer_id'] else row['mean'],\n",
    "    axis=1)\n",
    "df_profile_overall = df_profile_overall[['Overall Dataset']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "80bffaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster_summary = df_features.groupby('cluster_ids').describe().T.reset_index()\n",
    "df_cluster_summary = df_cluster_summary.rename(columns={'level_0':'column','level_1':'metric'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a90eb267",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep median for age & hh_income, count for customer id\n",
    "df_cluster_summary = df_cluster_summary.query(\n",
    "    '''\n",
    "        (column.isin([\"age\",\"hh_income\"]) & metric == \"50%\") | \\\n",
    "        (column.isin([\"customer_id\"]) & metric == \"count\") | \\\n",
    "        (~column.isin([\"customer_id\",\"age\",\"hh_income\"]) & metric == \"mean\")\n",
    "    ''',\n",
    "    engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "741ffcb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster_summary['Metric'] = df_cluster_summary.apply(\n",
    "    lambda row: 'median' if row['metric']=='50%' else row['metric'],\n",
    "    axis=1)\n",
    "df_cluster_summary = df_cluster_summary.set_index('column')\n",
    "df_cluster_summary = df_cluster_summary.drop('metric', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4c525d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_profile = df_cluster_summary.join(df_profile_overall) # joins on Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4035df0",
   "metadata": {},
   "source": [
    "# Ver Excel kmeans_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "06c272eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_profile.to_csv('./files/kmeans_profiling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "61efd2d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/clustering_features.csv').set_index('customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "12f3c924",
   "metadata": {},
   "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>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>orders</th>\n",
       "      <th>unique_products_bought</th>\n",
       "      <th>unique_payments_used</th>\n",
       "      <th>unique_categories_bought</th>\n",
       "      <th>aov</th>\n",
       "      <th>aur</th>\n",
       "      <th>upt</th>\n",
       "      <th>category_a_sales</th>\n",
       "      <th>category_b_sales</th>\n",
       "      <th>category_c_sales</th>\n",
       "      <th>category_d_sales</th>\n",
       "      <th>category_e_sales</th>\n",
       "      <th>category_a_units</th>\n",
       "      <th>category_b_units</th>\n",
       "      <th>category_c_units</th>\n",
       "      <th>category_d_units</th>\n",
       "      <th>category_e_units</th>\n",
       "      <th>payment_cash</th>\n",
       "      <th>payment_credit</th>\n",
       "      <th>payment_debit</th>\n",
       "      <th>payment_gc</th>\n",
       "      <th>payment_others</th>\n",
       "      <th>email_subscribed</th>\n",
       "      <th>omni_shopper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2395</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>217.727273</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.480167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068894</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.519833</td>\n",
       "      <td>0.167015</td>\n",
       "      <td>0.313152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4815</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>687.857143</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>0.721703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.247144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.031153</td>\n",
       "      <td>0.704050</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.077882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>428.500000</td>\n",
       "      <td>204.047619</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.240373</td>\n",
       "      <td>0.175029</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.210035</td>\n",
       "      <td>0.065344</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.263711</td>\n",
       "      <td>0.309218</td>\n",
       "      <td>0.147025</td>\n",
       "      <td>0.280047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12000</td>\n",
       "      <td>44</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>571.428571</td>\n",
       "      <td>272.727273</td>\n",
       "      <td>2.095238</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.028333</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.159091</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.345000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1700</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>212.500000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  units  orders  unique_products_bought  \\\n",
       "customer_id                                                 \n",
       "1             2395     11       5                       5   \n",
       "2             4815     15       7                       6   \n",
       "3             4285     21      10                       9   \n",
       "4            12000     44      21                      15   \n",
       "5             1700      8       3                       3   \n",
       "\n",
       "             unique_payments_used  unique_categories_bought         aov  \\\n",
       "customer_id                                                               \n",
       "1                               3                         3  479.000000   \n",
       "2                               4                         3  687.857143   \n",
       "3                               4                         5  428.500000   \n",
       "4                               4                         5  571.428571   \n",
       "5                               2                         2  566.666667   \n",
       "\n",
       "                    aur       upt  category_a_sales  category_b_sales  \\\n",
       "customer_id                                                             \n",
       "1            217.727273  2.200000          0.480167               NaN   \n",
       "2            321.000000  2.142857          0.721703               NaN   \n",
       "3            204.047619  2.100000          0.240373          0.175029   \n",
       "4            272.727273  2.095238          0.575000          0.067500   \n",
       "5            212.500000  2.666667               NaN          0.264706   \n",
       "\n",
       "             category_c_sales  category_d_sales  category_e_sales  \\\n",
       "customer_id                                                         \n",
       "1                    0.450939               NaN          0.068894   \n",
       "2                    0.247144               NaN          0.031153   \n",
       "3                    0.309218          0.210035          0.065344   \n",
       "4                    0.158333          0.170833          0.028333   \n",
       "5                    0.735294               NaN               NaN   \n",
       "\n",
       "             category_a_units  category_b_units  category_c_units  \\\n",
       "customer_id                                                         \n",
       "1                    0.363636               NaN          0.363636   \n",
       "2                    0.466667               NaN          0.333333   \n",
       "3                    0.190476          0.238095          0.238095   \n",
       "4                    0.409091          0.159091          0.181818   \n",
       "5                         NaN          0.375000          0.625000   \n",
       "\n",
       "             category_d_units  category_e_units  payment_cash  payment_credit  \\\n",
       "customer_id                                                                     \n",
       "1                         NaN          0.272727           NaN        0.519833   \n",
       "2                         NaN          0.200000      0.031153        0.704050   \n",
       "3                    0.095238          0.238095           NaN        0.263711   \n",
       "4                    0.113636          0.136364      0.066667        0.345000   \n",
       "5                         NaN               NaN           NaN             NaN   \n",
       "\n",
       "             payment_debit  payment_gc  payment_others  email_subscribed  \\\n",
       "customer_id                                                                \n",
       "1                 0.167015    0.313152             NaN                 0   \n",
       "2                 0.186916    0.077882             NaN                 1   \n",
       "3                 0.309218    0.147025        0.280047                 0   \n",
       "4                 0.370000    0.218333             NaN                 1   \n",
       "5                 0.735294    0.264706             NaN                 0   \n",
       "\n",
       "             omni_shopper  \n",
       "customer_id                \n",
       "1                       0  \n",
       "2                       1  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "85fb9a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill na\n",
    "df = df.fillna(0)\n",
    "\n",
    "# scaling\n",
    "cols_scale = [\n",
    "    'sales','units','upt','aur','aov','unique_categories_bought','unique_payments_used',\n",
    "    'unique_products_bought','orders']\n",
    "\n",
    "# Load scaler  from file\n",
    "pkl_filename = \"./files/model_objects/kmeans_scaler_model.pkl\"\n",
    "with open(pkl_filename, 'rb') as file:\n",
    "    scaler = pickle.load(file)\n",
    "\n",
    "df[cols_scale] = scaler.transform(df[cols_scale])\n",
    "\n",
    "# Load k-means from file\n",
    "pkl_filename = \"./files/model_objects/kmeans_model.pkl\"\n",
    "with open(pkl_filename, 'rb') as file:\n",
    "    kmeans = pickle.load(file)\n",
    "\n",
    "df['cluster_ids'] = kmeans.predict(df)\n",
    "\n",
    "# save labels\n",
    "df[['cluster_ids']].to_csv('./files/kmeans_labels.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1642d1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    293\n",
       "4    284\n",
       "2    194\n",
       "1     92\n",
       "5     82\n",
       "3     55\n",
       "Name: cluster_ids, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cluster_ids.value_counts()"
   ]
  }
 ],
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