uygulamalar/01_veriyi_anlama.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# giriş - müşteri analitiği çalışmaları"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* müşteri verileri anahtar perfomans göstergeleri:\n",
    "  * müşteri yaşaboyu değeri\n",
    "  * aylık ciro\n",
    "  * müşteri aktifliği\n",
    "  * elde tutma oranı\n",
    "* müşteri segmentasyon\n",
    "* müşteri kaybı tahmini\n",
    "* sepet analizi\n",
    "* ürün öneri sistemleri\n",
    "* a/b testleri\n",
    "* kampanya optimizasyonu\n",
    "* ciro tahmini\n",
    "* bir sonraki alışveriş zamanı tahmini\n",
    "* uplift modelleme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gerekli kütüphaneler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uyarı ayarı\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# veri işleme\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# istatistik\n",
    "import scipy as sc\n",
    "import hypothetical\n",
    "import pingouin\n",
    "import statsmodels as sm\n",
    "\n",
    "# veri görselleştirme\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from IPython.display import HTML, display\n",
    "\n",
    "# kütüphane ayarları\n",
    "pd.set_option('display.max_column',None)\n",
    "pd.set_option('display.max_row',None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# verinin çalışma ortamına alınması"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2009-2011 dönemi verileri **veri** isimli değişken olarak çalışma ortamına alınır:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri = pd.read_csv(\"dataset/online_retail_2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# veri seti hakkında ön bilgi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Çevrimiçi Perakende (Online Retail) 2](https://archive.ics.uci.edu/ml/datasets/Online+Retail+II) veri seti, Birleşik Krallık merkezli ve tescilli, mağaza dışı bir çevrimiçi perakende için 01/12/2009 ve 09/12/2011 tarihleri arasında gerçekleşen tüm işlemleri içerir. Şirket esas olarak her durumda benzersiz hediyelik eşya satmaktadır. Firmanın birçok müşterisi toptancıdır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "veri alanları:\n",
    "\n",
    "* InvoiceNo: Fatura numarası. Nominal. Her işleme benzersiz şekilde atanan 6 basamaklı bir sayı. Bu kod 'c' harfiyle başlıyorsa, bir iptal olduğunu gösterir.\n",
    "* StockCode: Ürün (öğe) kodu. Nominal. Her farklı ürüne benzersiz şekilde atanmış 5 basamaklı bir sayı.\n",
    "* Description: Ürün (öğe) adı. Nominal.\n",
    "* Quantity: İşlem başına her bir ürünün (öğenin) miktarı. Sayısal.\n",
    "* InvoiceDate: Fatura tarihi ve saati. Sayısal. Bir işlemin oluşturulduğu gün ve saat.\n",
    "* UnitPrice: Birim fiyat. Sayısal. Birim başına ürün fiyatı (£).\n",
    "* CustomerID: Müşteri numarası. Nominal. Her müşteriye benzersiz şekilde atanmış 5 basamaklı bir integral numarası.\n",
    "* Country: Ülke adı. Nominal. Müşterinin ikamet ettiği ülkenin adı."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "veri seti üzerinde çalışmalar:\n",
    "\n",
    "* Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link].\n",
    "* Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link].\n",
    "* Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019.\n",
    "* Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019.\n",
    "* Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# verinin incelenmesi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verinin boyutu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "veri seti 1067371 satır, 8 sütun veriden oluşuyor.\n"
     ]
    }
   ],
   "source": [
    "print(f\"veri seti {veri.shape[0]} satır, {veri.shape[1]} sütun veriden oluşuyor.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verideki ilk 5 gözlem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "           InvoiceDate  Price  Customer ID         Country  \n",
       "0  2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3  2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4  2009-12-01 07:45:00   1.25      13085.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verideki son 5 gözlem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1067366</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067367</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067368</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067369</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067370</th>\n",
       "      <td>581587</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice StockCode                      Description  Quantity  \\\n",
       "1067366  581587     22899     CHILDREN'S APRON DOLLY GIRL          6   \n",
       "1067367  581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "1067368  581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "1067369  581587     22138    BAKING SET 9 PIECE RETROSPOT          3   \n",
       "1067370  581587      POST                          POSTAGE         1   \n",
       "\n",
       "                 InvoiceDate  Price  Customer ID Country  \n",
       "1067366  2011-12-09 12:50:00   2.10      12680.0  France  \n",
       "1067367  2011-12-09 12:50:00   4.15      12680.0  France  \n",
       "1067368  2011-12-09 12:50:00   4.15      12680.0  France  \n",
       "1067369  2011-12-09 12:50:00   4.95      12680.0  France  \n",
       "1067370  2011-12-09 12:50:00  18.00      12680.0  France  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "veri setindeki değişken türleri ve doluluk hakkında bir bilgi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1067371 entries, 0 to 1067370\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count    Dtype  \n",
      "---  ------       --------------    -----  \n",
      " 0   Invoice      1067371 non-null  object \n",
      " 1   StockCode    1067371 non-null  object \n",
      " 2   Description  1062989 non-null  object \n",
      " 3   Quantity     1067371 non-null  int64  \n",
      " 4   InvoiceDate  1067371 non-null  object \n",
      " 5   Price        1067371 non-null  float64\n",
      " 6   Customer ID  824364 non-null   float64\n",
      " 7   Country      1067371 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 65.1+ MB\n"
     ]
    }
   ],
   "source": [
    "veri.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "veride boş değerler var mı?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer ID    243007\n",
      "Description      4382\n",
      "Country             0\n",
      "Price               0\n",
      "InvoiceDate         0\n",
      "Quantity            0\n",
      "StockCode           0\n",
      "Invoice             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(veri.isnull().sum().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tekil müşteri sayısı:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tekil müşteri sayısı:  5942\n"
     ]
    }
   ],
   "source": [
    "print('tekil müşteri sayısı: ', veri['Customer ID'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tekil işlem sayısı:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tekil işlem sayısı:  53628\n"
     ]
    }
   ],
   "source": [
    "print('tekil işlem sayısı: ', veri['Invoice'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "müşteri başına işlem sayısı:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tekil işlem sayısı:  9.03\n"
     ]
    }
   ],
   "source": [
    "print('tekil işlem sayısı: ', round(veri['Invoice'].nunique() / veri['Customer ID'].nunique(), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "işlemler için toplam fiyat:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri[\"TotalPrice\"] = veri[\"Quantity\"]*veri[\"Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "özet istatistikler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>Price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.067371e+06</td>\n",
       "      <td>1.067371e+06</td>\n",
       "      <td>1.067371e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.649388e+00</td>\n",
       "      <td>9.938898e+00</td>\n",
       "      <td>1.806987e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.235531e+02</td>\n",
       "      <td>1.727058e+02</td>\n",
       "      <td>2.924202e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.359436e+04</td>\n",
       "      <td>-8.099500e+04</td>\n",
       "      <td>-1.684696e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.250000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.750000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>9.900000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.150000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.770000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.897000e+04</td>\n",
       "      <td>8.099500e+04</td>\n",
       "      <td>1.684696e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price      Quantity    TotalPrice\n",
       "count  1.067371e+06  1.067371e+06  1.067371e+06\n",
       "mean   4.649388e+00  9.938898e+00  1.806987e+01\n",
       "std    1.235531e+02  1.727058e+02  2.924202e+02\n",
       "min   -5.359436e+04 -8.099500e+04 -1.684696e+05\n",
       "25%    1.250000e+00  1.000000e+00  3.750000e+00\n",
       "50%    2.100000e+00  3.000000e+00  9.900000e+00\n",
       "75%    4.150000e+00  1.000000e+01  1.770000e+01\n",
       "max    3.897000e+04  8.099500e+04  1.684696e+05"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[['Price', 'Quantity', 'TotalPrice']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ürün başına ortalama ~ 10 birim adet satılmış.\n",
    "* müşterilerin % 25'i, fiyatı 3,75 £ olan yalnızca bir birim satın almış.\n",
    "* müşterilerin % 50'si, fiyatı ortalama 2,10 £ olan 3 birimden daha azını satın almış.\n",
    "* müşterilerin % 75'i, fiyatı 4,15 sterlin olan 10 birimden daha azını satın almış."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fiyat ve miktar arasındaki korelasyon (?):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>0.061029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>-0.001319</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.762623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPrice</th>\n",
       "      <td>0.061029</td>\n",
       "      <td>0.762623</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price  Quantity  TotalPrice\n",
       "Price       1.000000 -0.001319    0.061029\n",
       "Quantity   -0.001319  1.000000    0.762623\n",
       "TotalPrice  0.061029  0.762623    1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[['Price', 'Quantity', 'TotalPrice']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapiro-Wilk normallik testi:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$W = \\frac{(\\sum_{i=1}^n a_i x_{i})^2}\n",
    "{\\sum_{i=1}^n (x_i - \\overline{x})^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$(a_1, ..., a_n) = \\frac{m^TV^{-1}}{(m^TV^{-1}V^{-1}m)^{1/2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TotalPrice</th>\n",
       "      <td>2.071447e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       W  pval  normal\n",
       "TotalPrice  2.071447e+06   0.0   False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pingouin.normality(veri['TotalPrice'], method='normaltest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw= {\"height_ratios\": (0.2, 1)})\n",
    "mean = veri['TotalPrice'].mean()\n",
    "median = veri['TotalPrice'].median()\n",
    "mode = veri['TotalPrice'].mode()[0]\n",
    "\n",
    "sns.boxplot(veri['TotalPrice'], ax=ax_box)\n",
    "ax_box.axvline(mean, color='r', linestyle='--')\n",
    "ax_box.axvline(median, color='g', linestyle='-')\n",
    "ax_box.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "sns.distplot(veri['TotalPrice'], ax=ax_hist)\n",
    "ax_hist.axvline(mean, color='r', linestyle='--')\n",
    "ax_hist.axvline(median, color='g', linestyle='-')\n",
    "ax_hist.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "plt.legend({'Mean':mean,'Median':median,'Mode':mode})\n",
    "\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tekil ürünler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tekil ürünler:  5698\n",
      "\n",
      " ['15CM CHRISTMAS GLASS BALL 20 LIGHTS' 'PINK CHERRY LIGHTS'\n",
      " ' WHITE CHERRY LIGHTS' ... 'mixed up'\n",
      " 'CREAM HANGING HEART T-LIGHT HOLDER' 'PAPER CRAFT , LITTLE BIRDIE']\n"
     ]
    }
   ],
   "source": [
    "print('tekil ürünler: ', veri['Description'].nunique())\n",
    "print( '\\n', veri['Description'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "müşteriler en çok hangi ürünleri tercih etmişler?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "en çok alınan ürünler: \n",
      "\n",
      "WHITE HANGING HEART T-LIGHT HOLDER    5918\n",
      "REGENCY CAKESTAND 3 TIER              4412\n",
      "JUMBO BAG RED RETROSPOT               3469\n",
      "ASSORTED COLOUR BIRD ORNAMENT         2958\n",
      "PARTY BUNTING                         2765\n",
      "STRAWBERRY CERAMIC TRINKET BOX        2613\n",
      "LUNCH BAG  BLACK SKULL.               2529\n",
      "JUMBO STORAGE BAG SUKI                2434\n",
      "HEART OF WICKER SMALL                 2319\n",
      "JUMBO SHOPPER VINTAGE RED PAISLEY     2297\n",
      "60 TEATIME FAIRY CAKE CASES           2271\n",
      "PAPER CHAIN KIT 50'S CHRISTMAS        2215\n",
      "LUNCH BAG SPACEBOY DESIGN             2206\n",
      "REX CASH+CARRY JUMBO SHOPPER          2199\n",
      "HOME BUILDING BLOCK WORD              2192\n",
      "Name: Description, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "en_çok_alınan = veri['Description'].value_counts().head(15)\n",
    "\n",
    "print('en çok alınan ürünler: \\n')\n",
    "print(en_çok_alınan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'en çok satılan ürünler')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,6))\n",
    "en_çok_alınan.plot(kind='barh').invert_yaxis()\n",
    "plt.title('en çok satılan ürünler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><td><img src='img/white_hanging_heart.jpg'></td><td><img src='img/regency cakestand.jpg'></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\"<table><tr><td><img src='img/white_hanging_heart.jpg'></td><td><img src='img/regency cakestand.jpg'></td></tr></table>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ürünlerin satıldığı farklı ülkeler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom          981330\n",
       "EIRE                     17866\n",
       "Germany                  17624\n",
       "France                   14330\n",
       "Netherlands               5140\n",
       "Spain                     3811\n",
       "Switzerland               3189\n",
       "Belgium                   3123\n",
       "Portugal                  2620\n",
       "Australia                 1913\n",
       "Channel Islands           1664\n",
       "Italy                     1534\n",
       "Norway                    1455\n",
       "Sweden                    1364\n",
       "Cyprus                    1176\n",
       "Finland                   1049\n",
       "Austria                    938\n",
       "Denmark                    817\n",
       "Unspecified                756\n",
       "Greece                     663\n",
       "Japan                      582\n",
       "USA                        535\n",
       "Poland                     535\n",
       "United Arab Emirates       500\n",
       "Israel                     371\n",
       "Hong Kong                  364\n",
       "Singapore                  346\n",
       "Malta                      299\n",
       "Iceland                    253\n",
       "Canada                     228\n",
       "Lithuania                  189\n",
       "RSA                        169\n",
       "Bahrain                    126\n",
       "Brazil                      94\n",
       "Thailand                    76\n",
       "Korea                       63\n",
       "European Community          61\n",
       "Lebanon                     58\n",
       "West Indies                 54\n",
       "Bermuda                     34\n",
       "Nigeria                     32\n",
       "Czech Republic              30\n",
       "Saudi Arabia                10\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri['Country'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ülke grupları:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ülke grupları\n",
    "avrupa_ülkeleri = ['Austria', 'Belgium', 'Cyprus', 'Czech Republic', 'Denmark',\n",
    "                   'EIRE', 'European Community', 'Finland', 'France', 'Germany',\n",
    "                   'Greece', 'Iceland','Italy', 'Lithuania', 'Malta', 'Netherlands',\n",
    "                   'Norway', 'Poland', 'Portugal', 'Spain', 'Sweden', 'Switzerland',\n",
    "                   'United Kingdom', 'Channel Islands']\n",
    "\n",
    "amerika_ülkeleri = ['Canada', 'USA', 'Brazil', 'Bermuda']\n",
    "\n",
    "asya_ülkeleri = ['Bahrain','Hong Kong', 'Japan', 'Saudi Arabia', 'Singapore', 'Thailand', 'United Arab Emirates']\n",
    "\n",
    "# ülke grupları fonksiyon\n",
    "def ülke_grubu(row):\n",
    "    global avrupa_ülkeleri\n",
    "    global amerika_ülkeleri\n",
    "    global asya_ülkeleri\n",
    "    \n",
    "    if row['Country'] in avrupa_ülkeleri:\n",
    "        return \"Europe\"\n",
    "    elif row['Country'] in amerika_ülkeleri:\n",
    "        return \"America\"\n",
    "    elif row['Country'] in asya_ülkeleri:\n",
    "        return \"Asia\"\n",
    "    else:\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri = veri.assign(CountryGroup=veri.apply(ülke_grubu, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Europe', 'America', 'Other', 'Asia'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri['CountryGroup'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "soru: acaba ülke grupları arasında, TotalPrice değişkeni için bir farklılık var mı?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* eşit varyans testi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>equal_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>levene</th>\n",
       "      <td>29.033413</td>\n",
       "      <td>9.205135e-19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                W          pval  equal_var\n",
       "levene  29.033413  9.205135e-19      False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pingouin.homoscedasticity(veri, dv='TotalPrice', group='CountryGroup', method='levene')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* eşit varyans yok, bu durumda, kruskal-wallis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>H</th>\n",
       "      <th>p-unc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kruskal</th>\n",
       "      <td>CountryGroup</td>\n",
       "      <td>3</td>\n",
       "      <td>2690.246166</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Source  ddof1            H  p-unc\n",
       "Kruskal  CountryGroup      3  2690.246166    0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pingouin.kruskal(veri, dv='TotalPrice', between='CountryGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": null,
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