KPMG_Virtual_Internship-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Library import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Library importing for Lenear Algebra and Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "import datetime\n",
    "\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "import warnings\n",
    "import math as ma\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries for Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic= pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\", sheet_name=\"CustomerDemographic\")\n",
    "transactions= pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\", sheet_name=\"Transactions\")\n",
    "customeraddress= pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\", sheet_name=\"CustomerAddress\")\n",
    "newcustomerlist= pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\", sheet_name=\"NewCustomerList\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "   past_3_years_bike_related_purchases        DOB               job_title  \\\n",
       "0                                   93 1953-10-12     Executive Secretary   \n",
       "1                                   81 1980-12-16  Administrative Officer   \n",
       "2                                   61 1954-01-20      Recruiting Manager   \n",
       "3                                   33 1961-10-03                     NaN   \n",
       "4                                   56 1977-05-13           Senior Editor   \n",
       "\n",
       "  job_industry_category     wealth_segment deceased_indicator  \\\n",
       "0                Health      Mass Customer                  N   \n",
       "1    Financial Services      Mass Customer                  N   \n",
       "2              Property      Mass Customer                  N   \n",
       "3                    IT      Mass Customer                  N   \n",
       "4                   NaN  Affluent Customer                  N   \n",
       "\n",
       "                                             default owns_car  tenure  \n",
       "0                                                 \"'      Yes    11.0  \n",
       "1                       <script>alert('hi')</script>      Yes    16.0  \n",
       "2                                2018-02-01 00:00:00      Yes    15.0  \n",
       "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No     7.0  \n",
       "4                                                NIL      Yes     8.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerdemographic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>36145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost  product_first_sold_date  \n",
       "0       71.49          53.62                  41245.0  \n",
       "1     2091.47         388.92                  41701.0  \n",
       "2     1793.43         248.82                  36361.0  \n",
       "3     1198.46         381.10                  36145.0  \n",
       "4     1765.30         709.48                  42226.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id              address  postcode            state    country  \\\n",
       "0            1   060 Morning Avenue      2016  New South Wales  Australia   \n",
       "1            2  6 Meadow Vale Court      2153  New South Wales  Australia   \n",
       "2            4   0 Holy Cross Court      4211              QLD  Australia   \n",
       "3            5  17979 Del Mar Point      2448  New South Wales  Australia   \n",
       "4            6     9 Oakridge Court      3216              VIC  Australia   \n",
       "\n",
       "   property_valuation  \n",
       "0                  10  \n",
       "1                  10  \n",
       "2                   9  \n",
       "3                   4  \n",
       "4                   9  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customeraddress.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Exploring Data Analysis(EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Column Headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(customerdemographic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic=customerdemographic.rename(columns={'first_name':'f_name','last_name':'l_name','past_3_years_bike_related_purchases':'past_bike_purchases','owns_car':'car_owner','job_industry_category':'job_idustry'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(customerdemographic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic=customerdemographic.iloc[1: ]\n",
    "customerdemographic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Value Find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total= customerdemographic.isnull().sum().sort_values(ascending=False)\n",
    "percentage_1=customerdemographic.isnull().sum()/customerdemographic.isnull().count()*100\n",
    "percentage_2=(round(percentage_1, 1).sort_values(ascending=False))\n",
    "missing_data=pd.concat([total, percentage_2], axis=1, keys=['total', \"%\"])\n",
    "missing_data.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "customerdemographic.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array to fill null values with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_NAN():\n",
    "    ret = []\n",
    "    temp = list(customerdemographic)\n",
    "    for each in temp:\n",
    "        ret.append(customerdemographic[each].isna().sum())\n",
    "    return ret\n",
    "\n",
    "NaN_list = check_NAN()\n",
    "print(NaN_list, len(NaN_list))\n",
    "\n",
    "# Now checking Columns\n",
    "\n",
    "check = []\n",
    "k     = -1\n",
    "for i in NaN_list:\n",
    "    k += 1\n",
    "    if i > 0:\n",
    "        check.append(k)\n",
    "        \n",
    "# Now checking Unique\n",
    "\n",
    "def check_unique():\n",
    "    temp = list(customerdemographic)\n",
    "    dict = {}\n",
    "    for i in range(3, 13):\n",
    "        ct = 0\n",
    "        if NaN_list[i] > 0:\n",
    "            ct = len(customerdemographic[temp[i]].unique()) - 1\n",
    "        else:\n",
    "            ct = len(customerdemographic[temp[i]].unique())\n",
    "        dict[temp[i]] = (ct, customerdemographic[temp[i]].unique())\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now to compare Gender Vs. Bike bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. F - Female, 2. M- Male, 3. U-Unknown in array\n",
    "\n",
    "gender = [0,0,0] \n",
    "for each in customerdemographic['gender']:\n",
    "    if each[0] == \"F\":\n",
    "        gender[0] += 1\n",
    "    elif each[0] == \"M\":\n",
    "        gender[1] += 1\n",
    "    else:\n",
    "        gender[2] += 1\n",
    "\n",
    "print(gender)\n",
    "\n",
    "def gender_female(x, pos):\n",
    "    \n",
    "    return int(x)\n",
    "\n",
    "bike_list = [0,0,0]\n",
    "bike_list[0] += customerdemographic['past_bike_purchases'][customerdemographic['gender']== 'Female'].sum() + customerdemographic['past_bike_purchases'][customerdemographic['gender']== 'F'].sum()\n",
    "bike_list[1] += customerdemographic['past_bike_purchases'][customerdemographic['gender']== 'Male'].sum() + customerdemographic['past_bike_purchases'][customerdemographicn['gender']== 'M'].sum()\n",
    "bike_list[2] += customerdemographic['past_bike_purchases'][customerdemographic['gender']== 'U'].sum()\n",
    "\n",
    "grapho = [gender[0], bike_list[0], gender[1], bike_list[1], gender[2], bike_list[2]]\n",
    "graphl = [gender[0]*10,bike_list[0], gender[1]*10, bike_list[1], gender[2]*10, bike_list[2]]\n",
    "colorr = (0.3,0.1,0.4,0.6)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The 'transaction_date' and 'product_first_sold_date' change to date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1970-01-01 11:27:25\n",
       "1   1970-01-01 11:35:01\n",
       "2   1970-01-01 10:06:01\n",
       "3   1970-01-01 10:02:25\n",
       "4   1970-01-01 11:43:46\n",
       "Name: product_first_sold_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert date columns from integer to datetime\n",
    "transactions['transaction_date'] = pd.to_datetime(transactions['transaction_date'], unit='s')\n",
    "transactions['transaction_date'].head()\n",
    "\n",
    "#convert date columns from integer to datetime\n",
    "transactions['product_first_sold_date'] = pd.to_datetime(transactions['product_first_sold_date'])\n",
    "transactions['product_first_sold_date'].head()"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>1970-01-01 11:27:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>1970-01-01 11:35:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>1970-01-01 10:06:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>1970-01-01 10:02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>1970-01-01 11:43:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost product_first_sold_date  \n",
       "0       71.49          53.62     1970-01-01 11:27:25  \n",
       "1     2091.47         388.92     1970-01-01 11:35:01  \n",
       "2     1793.43         248.82     1970-01-01 10:06:01  \n",
       "3     1198.46         381.10     1970-01-01 10:02:25  \n",
       "4     1765.30         709.48     1970-01-01 11:43:46  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>total</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>360</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <td>197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_cost</th>\n",
       "      <td>197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_size</th>\n",
       "      <td>197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_class</th>\n",
       "      <td>197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         total    %\n",
       "online_order               360  1.8\n",
       "product_first_sold_date    197  1.0\n",
       "standard_cost              197  1.0\n",
       "product_size               197  1.0\n",
       "product_class              197  1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total= transactions.isnull().sum().sort_values(ascending=False)\n",
    "percentage_1=transactions.isnull().sum()/transactions.isnull().count()*100\n",
    "percentage_2=(round(percentage_1, 1).sort_values(ascending=False))\n",
    "missing_data=pd.concat([total, percentage_2], axis=1, keys=['total', \"%\"])\n",
    "missing_data.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             0.0\n",
       "product_id                 0.0\n",
       "customer_id                0.0\n",
       "transaction_date           0.0\n",
       "online_order               0.0\n",
       "order_status               0.0\n",
       "brand                      0.0\n",
       "product_line               0.0\n",
       "product_class              0.0\n",
       "product_size               0.0\n",
       "list_price                 0.0\n",
       "standard_cost              0.0\n",
       "product_first_sold_date    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicate Values\n",
    "\n",
    "dups = transactions.duplicated()\n",
    "transactions[dups].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create RFM Features for each customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19071.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>18349.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>18052.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>17898.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2788</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>17258.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recency  Frequency  Monetary\n",
       "customer_id                              \n",
       "2183               8          1  19071.32\n",
       "1129              26          1  18349.27\n",
       "1597              25          1  18052.68\n",
       "941               33          1  17898.46\n",
       "2788              44          1  17258.94"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RFM = transactions.groupby('customer_id').agg({'transaction_date': lambda y: (transactions['transaction_date'].max().date() - y.max().date()).days,\n",
    "                                        'customer_id': lambda y: len(y.unique()),  \n",
    "                                        'list_price': lambda y: round(y.sum(),2)})\n",
    "df_RFM.columns = ['Recency', 'Frequency', 'Monetary']\n",
    "df_RFM = df_RFM.sort_values('Monetary', ascending=False)\n",
    "df_RFM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To automate the segmentation we will use 80% quantile for Recency and Monetary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Recency  Frequency  Monetary\n",
      "0.8     98.0        1.0   8764.25\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19071.32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>18349.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>18052.68</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>17898.46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2788</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>17258.94</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recency  Frequency  Monetary  R  F  M\n",
       "customer_id                                       \n",
       "2183               8          1  19071.32  2  2  2\n",
       "1129              26          1  18349.27  2  2  2\n",
       "1597              25          1  18052.68  2  2  2\n",
       "941               33          1  17898.46  2  2  2\n",
       "2788              44          1  17258.94  2  2  2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = df_RFM.quantile(q=[0.8])\n",
    "print(quantiles)\n",
    "df_RFM['R']=np.where(df_RFM['Recency']<=int(quantiles.Recency.values), 2, 1)\n",
    "df_RFM['F']=np.where(df_RFM['Frequency']>=int(quantiles.Frequency.values), 2, 1)\n",
    "df_RFM['M']=np.where(df_RFM['Monetary']>=int(quantiles.Monetary.values), 2, 1)\n",
    "df_RFM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate RFM score and sort customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>R</th>\n",
       "      <th>M</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMScore</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>22</th>\n",
       "      <td>653</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2144</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>650</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         customer_id  Frequency  Recency  R  M  Monetary\n",
       "RMScore                                                 \n",
       "22               653          1       32  2  2   10835.0\n",
       "21                47          1      123  1  2   10257.0\n",
       "12              2144          1       39  2  1    5468.0\n",
       "11               650          1      158  1  1    4423.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To do the 2x2 Matrix we will only use Recency & Monetary\n",
    "df_RFM['RMScore'] = df_RFM.M.map(str)+df_RFM.R.map(str)\n",
    "df_RFM = df_RFM.reset_index()\n",
    "df_RFM_SUM = df_RFM.groupby('RMScore').agg({'customer_id': lambda y: len(y.unique()),\n",
    "                                        'Frequency': lambda y: round(y.mean(),0),\n",
    "                                        'Recency': lambda y: round(y.mean(),0),\n",
    "                                        'R': lambda y: round(y.mean(),0),\n",
    "                                        'M': lambda y: round(y.mean(),0),\n",
    "                                        'Monetary': lambda y: round(y.mean(),0)})\n",
    "df_RFM_SUM = df_RFM_SUM.sort_values('RMScore', ascending=False)\n",
    "df_RFM_SUM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Value Matrix and explore some key numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R        1        2\n",
      "M                  \n",
      "2  10257.0  10835.0\n",
      "1   4423.0   5468.0\n"
     ]
    }
   ],
   "source": [
    "#Average Monetary Matrix\n",
    "\n",
    "df_RFM_M = df_RFM_SUM.pivot(index='M', columns='R', values='Monetary')\n",
    "df_RFM_M= df_RFM_M.reset_index().sort_values(['M'], ascending = False).set_index(['M'])\n",
    "print(df_RFM_M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Customer Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R    1     2\n",
      "M           \n",
      "2   47   653\n",
      "1  650  2144\n"
     ]
    }
   ],
   "source": [
    "df_RFM_C = df_RFM_SUM.pivot(index='M', columns='R', values='customer_id')\n",
    "df_RFM_C= df_RFM_C.reset_index().sort_values(['M'], ascending = False).set_index(['M'])\n",
    "print(df_RFM_C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recency Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>R</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "R    1   2\n",
       "M         \n",
       "2  123  32\n",
       "1  158  39"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RFM_R = df_RFM_SUM.pivot(index='M', columns='R', values='Recency')\n",
    "df_RFM_R= df_RFM_R.reset_index().sort_values(['M'], ascending = False).set_index(['M'])\n",
    "\n",
    "df_RFM_R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.distplot(df_RFM.Recency, kde=False, rug=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.distplot(df_RFM.Monetary, kde=False, rug=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The distribution of segments using the squarify plot :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'matplotlib' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-214e7236056a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msquarify\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoolwarm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mmin_i\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msq1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmax_i\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msq1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin_i\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_i\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'matplotlib' is not defined"
     ]
    }
   ],
   "source": [
    "import squarify\n",
    "cmap= matplotlib.cm.coolwarm\n",
    "min_i=min(sq1['customer_id'])\n",
    "max_i=max(sq1['customer_id'])\n",
    "norm=matplotlib.colors.Normalize(vmin=min_i, vmax=max_i)\n",
    "colors=[cmap(norm(value)) for value in sq1['customer_id']]\n",
    "fig=plt.gcf()\n",
    "ax=fig.add_subplot()\n",
    "fig.set_size_inches(12, 8)\n",
    "squarify.plot(sizes=sq1['customer_id'], label=sq1.Segment, alpha=1, color=colors)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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