From 3d91de860870d5d953beeb1d6299bf69bcda0ac4 Mon Sep 17 00:00:00 2001 From: Behniash Date: Sun, 9 Aug 2026 19:57:37 -0700 Subject: [PATCH 1/3] Add CLV analysis --- notebooks/CustomerLifetime.ipynb | 2094 ++++++++++++++++++++++++++++++ 1 file changed, 2094 insertions(+) create mode 100644 notebooks/CustomerLifetime.ipynb diff --git a/notebooks/CustomerLifetime.ipynb b/notebooks/CustomerLifetime.ipynb new file mode 100644 index 0000000..1b15fa8 --- /dev/null +++ b/notebooks/CustomerLifetime.ipynb @@ -0,0 +1,2094 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1601aa4d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from lifelines import KaplanMeierFitter\n", + "\n", + "\n", + "import warnings; warnings.filterwarnings('ignore')\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "df = pd.read_csv('../datasets/superstore.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ac7dedda", + "metadata": {}, + "outputs": [], + "source": [ + "df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n", + "df[\"postal_code\"] = df[\"postal_code\"].astype(str)\n", + "df[\"row_id\"] = df[\"row_id\"].astype(str)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "de7887f7", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"order_date\"] = pd.to_datetime(df[\"order_date\"], format='%m/%d/%Y')\n", + "df[\"ship_date\"] = pd.to_datetime(df[\"ship_date\"], format='%m/%d/%Y')\n", + "df[\"order_year\"] = df[\"order_date\"].dt.year\n", + "df[\"order_month\"] = df[\"order_date\"].dt.to_period(\"M\")\n", + "df[\"order_month_name\"] = (df[\"order_date\"].dt.month_name())\n", + "df[\"order_day\"] = (df[\"order_date\"].dt.day_name())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6a3e4d0f", + "metadata": {}, + "outputs": [], + "source": [ + "df.rename(columns={\"sales\" : \"revenue\"}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f956941", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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row_idorder_idorder_dateship_dateship_modecustomer_idcustomer_namesegmentcountrycitystatepostal_coderegionproduct_idcategorysub-categoryproduct_namerevenuequantitydiscountprofitorder_yearorder_monthorder_month_nameorder_day
01CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.913620162016-11NovemberTuesday
12CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs,...731.940030.00219.582020162016-11NovemberTuesday
23CA-2016-1386882016-06-122016-06-16Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036WestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters b...14.620020.006.871420162016-06JuneSunday
34US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.031020152015-10OctoberSunday
45US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.368020.202.516420152015-10OctoberSunday
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" + ], + "text/plain": [ + " row_id order_id order_date ship_date ship_mode customer_id \\\n", + "0 1 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 \n", + "1 2 CA-2016-152156 2016-11-08 2016-11-11 Second Class CG-12520 \n", + "2 3 CA-2016-138688 2016-06-12 2016-06-16 Second Class DV-13045 \n", + "3 4 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 \n", + "4 5 US-2015-108966 2015-10-11 2015-10-18 Standard Class SO-20335 \n", + "\n", + " customer_name segment country city state \\\n", + "0 Claire Gute Consumer United States Henderson Kentucky \n", + "1 Claire Gute Consumer United States Henderson Kentucky \n", + "2 Darrin Van Huff Corporate United States Los Angeles California \n", + "3 Sean O'Donnell Consumer United States Fort Lauderdale Florida \n", + "4 Sean O'Donnell Consumer United States Fort Lauderdale Florida \n", + "\n", + " postal_code region product_id category sub-category \\\n", + "0 42420 South FUR-BO-10001798 Furniture Bookcases \n", + "1 42420 South FUR-CH-10000454 Furniture Chairs \n", + "2 90036 West OFF-LA-10000240 Office Supplies Labels \n", + "3 33311 South FUR-TA-10000577 Furniture Tables \n", + "4 33311 South OFF-ST-10000760 Office Supplies Storage \n", + "\n", + " product_name revenue quantity \\\n", + "0 Bush Somerset Collection Bookcase 261.9600 2 \n", + "1 Hon Deluxe Fabric Upholstered Stacking Chairs,... 731.9400 3 \n", + "2 Self-Adhesive Address Labels for Typewriters b... 14.6200 2 \n", + "3 Bretford CR4500 Series Slim Rectangular Table 957.5775 5 \n", + "4 Eldon Fold 'N Roll Cart System 22.3680 2 \n", + "\n", + " discount profit order_year order_month order_month_name order_day \n", + "0 0.00 41.9136 2016 2016-11 November Tuesday \n", + "1 0.00 219.5820 2016 2016-11 November Tuesday \n", + "2 0.00 6.8714 2016 2016-06 June Sunday \n", + "3 0.45 -383.0310 2015 2015-10 October Sunday \n", + "4 0.20 2.5164 2015 2015-10 October Sunday " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e794bd3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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customer_idfirst_purchaselast_purchasetotal_orderstotal_revenuetotal_profittotal_quantity
0AA-103152014-03-312017-06-2955563.560-362.882530
1AA-103752014-04-212017-12-1191056.390277.382441
2AA-104802014-05-042017-04-1541790.512435.827436
3AA-106452014-06-222017-11-0565086.935857.803364
4AB-100152014-02-182016-11-103886.156129.346513
........................
788XP-218652014-01-202017-11-17112374.658621.2300100
789YC-218952014-11-172017-12-2655454.3501305.629031
790YS-218802015-01-122017-12-2186720.4441778.292358
791ZC-219102014-10-132017-11-06138025.707-1032.1490105
792ZD-219252014-08-272017-06-1151493.944249.130732
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793 rows × 7 columns

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" + ], + "text/plain": [ + " customer_id first_purchase last_purchase total_orders total_revenue \\\n", + "0 AA-10315 2014-03-31 2017-06-29 5 5563.560 \n", + "1 AA-10375 2014-04-21 2017-12-11 9 1056.390 \n", + "2 AA-10480 2014-05-04 2017-04-15 4 1790.512 \n", + "3 AA-10645 2014-06-22 2017-11-05 6 5086.935 \n", + "4 AB-10015 2014-02-18 2016-11-10 3 886.156 \n", + ".. ... ... ... ... ... \n", + "788 XP-21865 2014-01-20 2017-11-17 11 2374.658 \n", + "789 YC-21895 2014-11-17 2017-12-26 5 5454.350 \n", + "790 YS-21880 2015-01-12 2017-12-21 8 6720.444 \n", + "791 ZC-21910 2014-10-13 2017-11-06 13 8025.707 \n", + "792 ZD-21925 2014-08-27 2017-06-11 5 1493.944 \n", + "\n", + " total_profit total_quantity \n", + "0 -362.8825 30 \n", + "1 277.3824 41 \n", + "2 435.8274 36 \n", + "3 857.8033 64 \n", + "4 129.3465 13 \n", + ".. ... ... \n", + "788 621.2300 100 \n", + "789 1305.6290 31 \n", + "790 1778.2923 58 \n", + "791 -1032.1490 105 \n", + "792 249.1307 32 \n", + "\n", + "[793 rows x 7 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_history = df.groupby(\"customer_id\").agg(\n", + " first_purchase=(\"order_date\", \"min\"),\n", + " last_purchase=(\"order_date\", \"max\"),\n", + " total_orders=(\"order_id\", \"nunique\"),\n", + " total_revenue=(\"revenue\", \"sum\"),\n", + " total_profit=(\"profit\", \"sum\"),\n", + " total_quantity=(\"quantity\", \"sum\")\n", + " ).reset_index()\n", + "customer_history" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "459e3945", + "metadata": {}, + "outputs": [], + "source": [ + "threshold = 1\n", + "array = df['order_id'].value_counts()\n", + "keep_ids = array[array > threshold].index\n", + "df_unique = df[df['order_id'].isin(keep_ids)]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "22e98df3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_datedays_between_orders
7392014-01-04NaN
7402014-01-040.0
7412014-01-040.0
16942015-09-25629.0
16952015-09-250.0
16962015-09-250.0
9742017-10-05741.0
9752017-10-050.0
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" + ], + "text/plain": [ + " order_date days_between_orders\n", + "739 2014-01-04 NaN\n", + "740 2014-01-04 0.0\n", + "741 2014-01-04 0.0\n", + "1694 2015-09-25 629.0\n", + "1695 2015-09-25 0.0\n", + "1696 2015-09-25 0.0\n", + "974 2017-10-05 741.0\n", + "975 2017-10-05 0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_new = df_unique[['customer_id', 'order_date']].sort_values(\"order_date\")\n", + "df_new[\"days_between_orders\"] = (df_new.groupby(\"customer_id\")[\"order_date\"].diff().dt.days)\n", + "\n", + "cid = df_new['customer_id'].iloc[0]\n", + "df_new[df_new['customer_id'] == cid][['order_date', 'days_between_orders']]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "575852a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer_id\n", + "SF-20065 382.666667\n", + "SL-20155 371.333333\n", + "JL-15175 361.333333\n", + "AC-10420 345.000000\n", + "AR-10405 330.000000\n", + " ... \n", + "VT-21700 0.000000\n", + "TC-21145 0.000000\n", + "CS-12175 0.000000\n", + "CS-11860 0.000000\n", + "LD-17005 0.000000\n", + "Name: days_between_orders, Length: 769, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "purchase_gap = df_new.groupby(\"customer_id\")[\"days_between_orders\"].mean()\n", + "purchase_gap.sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b4181c81", + "metadata": {}, + "outputs": [], + "source": [ + "snapshot_date = df_unique[\"order_date\"].max() + pd.Timedelta(days=1)\n", + "customer_history[\"recency\"] = (snapshot_date - customer_history.last_purchase).dt.days" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "efd8818a", + "metadata": {}, + "outputs": [], + "source": [ + "customer_history[\"churn_90\"] = (customer_history[\"recency\"] > 90).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7515e000", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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customer_idfirst_purchaselast_purchasetotal_orderstotal_revenuetotal_profittotal_quantityrecencychurn_90
0AA-103152014-03-312017-06-2955563.560-362.8825301851
1AA-103752014-04-212017-12-1191056.390277.382441200
2AA-104802014-05-042017-04-1541790.512435.8274362601
3AA-106452014-06-222017-11-0565086.935857.803364560
4AB-100152014-02-182016-11-103886.156129.3465134161
..............................
788XP-218652014-01-202017-11-17112374.658621.2300100440
789YC-218952014-11-172017-12-2655454.3501305.62903150
790YS-218802015-01-122017-12-2186720.4441778.292358100
791ZC-219102014-10-132017-11-06138025.707-1032.1490105550
792ZD-219252014-08-272017-06-1151493.944249.1307322031
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793 rows × 9 columns

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customer_idorder_datedays_between_orders
739PO-191952014-01-04NaN
740PO-191952014-01-040.0
741PO-191952014-01-040.0
7480ME-173202014-01-06NaN
7479ME-173202014-01-060.0
............
1297EB-139752017-12-30446.0
1296EB-139752017-12-300.0
908PO-188652017-12-30147.0
907PO-188652017-12-300.0
906PO-188652017-12-300.0
\n", + "

7456 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " customer_id order_date days_between_orders\n", + "739 PO-19195 2014-01-04 NaN\n", + "740 PO-19195 2014-01-04 0.0\n", + "741 PO-19195 2014-01-04 0.0\n", + "7480 ME-17320 2014-01-06 NaN\n", + "7479 ME-17320 2014-01-06 0.0\n", + "... ... ... ...\n", + "1297 EB-13975 2017-12-30 446.0\n", + "1296 EB-13975 2017-12-30 0.0\n", + "908 PO-18865 2017-12-30 147.0\n", + "907 PO-18865 2017-12-30 0.0\n", + "906 PO-18865 2017-12-30 0.0\n", + "\n", + "[7456 rows x 3 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_new" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "01c34f8b", + "metadata": {}, + "outputs": [], + "source": [ + "df_unique = df_unique.merge(df_new[[\"customer_id\", \"days_between_orders\"]], on=\"customer_id\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "66e33a9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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avg_gapstd_gapfrequency
customer_id
AA-10315131.777778209.7214734
AA-10375115.777778211.7592324
AA-104804.44444412.6412122
AA-1064550.312500101.9493845
AB-10015244.750000434.9321632
............
XP-2186566.380952170.1933885
YC-21895164.750000292.7684332
YS-21880125.833333228.8129413
ZC-2191041.48148195.17162410
ZD-21925113.500000216.3461833
\n", + "

769 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " avg_gap std_gap frequency\n", + "customer_id \n", + "AA-10315 131.777778 209.721473 4\n", + "AA-10375 115.777778 211.759232 4\n", + "AA-10480 4.444444 12.641212 2\n", + "AA-10645 50.312500 101.949384 5\n", + "AB-10015 244.750000 434.932163 2\n", + "... ... ... ...\n", + "XP-21865 66.380952 170.193388 5\n", + "YC-21895 164.750000 292.768433 2\n", + "YS-21880 125.833333 228.812941 3\n", + "ZC-21910 41.481481 95.171624 10\n", + "ZD-21925 113.500000 216.346183 3\n", + "\n", + "[769 rows x 3 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_stats = (df_unique.sort_values([\"order_date\"]).groupby(\"customer_id\")\n", + " .agg(\n", + " avg_gap=(\"days_between_orders\",\"mean\"),\n", + " std_gap=(\"days_between_orders\",\"std\"),\n", + " frequency=(\"order_id\",\"nunique\")\n", + " )\n", + ")\n", + "customer_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bf30538f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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avg_gapstd_gapfrequencychurn_threshold
customer_id
AA-10315131.777778209.7214734341.499251
AA-10375115.777778211.7592324327.537010
AA-104804.44444412.641212217.085657
AA-1064550.312500101.9493845152.261884
AB-10015244.750000434.9321632679.682163
...............
XP-2186566.380952170.1933885236.574341
YC-21895164.750000292.7684332457.518433
YS-21880125.833333228.8129413354.646274
ZC-2191041.48148195.17162410136.653106
ZD-21925113.500000216.3461833329.846183
\n", + "

769 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " avg_gap std_gap frequency churn_threshold\n", + "customer_id \n", + "AA-10315 131.777778 209.721473 4 341.499251\n", + "AA-10375 115.777778 211.759232 4 327.537010\n", + "AA-10480 4.444444 12.641212 2 17.085657\n", + "AA-10645 50.312500 101.949384 5 152.261884\n", + "AB-10015 244.750000 434.932163 2 679.682163\n", + "... ... ... ... ...\n", + "XP-21865 66.380952 170.193388 5 236.574341\n", + "YC-21895 164.750000 292.768433 2 457.518433\n", + "YS-21880 125.833333 228.812941 3 354.646274\n", + "ZC-21910 41.481481 95.171624 10 136.653106\n", + "ZD-21925 113.500000 216.346183 3 329.846183\n", + "\n", + "[769 rows x 4 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_stats[\"churn_threshold\"] = (customer_stats[\"avg_gap\"] + customer_stats[\"std_gap\"])\n", + "customer_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "452c24fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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customer_idfirst_purchaselast_purchasetotal_orderstotal_revenuetotal_profittotal_quantityrecencychurn_90churn_threshold
0AA-103152014-03-312017-06-2955563.560-362.8825301851341.499251
1AA-103752014-04-212017-12-1191056.390277.382441200327.537010
2AA-104802014-05-042017-04-1541790.512435.827436260117.085657
3AA-106452014-06-222017-11-0565086.935857.803364560152.261884
4AB-100152014-02-182016-11-103886.156129.3465134161679.682163
.................................
764XP-218652014-01-202017-11-17112374.658621.2300100440236.574341
765YC-218952014-11-172017-12-2655454.3501305.62903150457.518433
766YS-218802015-01-122017-12-2186720.4441778.292358100354.646274
767ZC-219102014-10-132017-11-06138025.707-1032.1490105550136.653106
768ZD-219252014-08-272017-06-1151493.944249.1307322031329.846183
\n", + "

769 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " customer_id first_purchase last_purchase total_orders total_revenue \\\n", + "0 AA-10315 2014-03-31 2017-06-29 5 5563.560 \n", + "1 AA-10375 2014-04-21 2017-12-11 9 1056.390 \n", + "2 AA-10480 2014-05-04 2017-04-15 4 1790.512 \n", + "3 AA-10645 2014-06-22 2017-11-05 6 5086.935 \n", + "4 AB-10015 2014-02-18 2016-11-10 3 886.156 \n", + ".. ... ... ... ... ... \n", + "764 XP-21865 2014-01-20 2017-11-17 11 2374.658 \n", + "765 YC-21895 2014-11-17 2017-12-26 5 5454.350 \n", + "766 YS-21880 2015-01-12 2017-12-21 8 6720.444 \n", + "767 ZC-21910 2014-10-13 2017-11-06 13 8025.707 \n", + "768 ZD-21925 2014-08-27 2017-06-11 5 1493.944 \n", + "\n", + " total_profit total_quantity recency churn_90 churn_threshold \n", + "0 -362.8825 30 185 1 341.499251 \n", + "1 277.3824 41 20 0 327.537010 \n", + "2 435.8274 36 260 1 17.085657 \n", + "3 857.8033 64 56 0 152.261884 \n", + "4 129.3465 13 416 1 679.682163 \n", + ".. ... ... ... ... ... \n", + "764 621.2300 100 44 0 236.574341 \n", + "765 1305.6290 31 5 0 457.518433 \n", + "766 1778.2923 58 10 0 354.646274 \n", + "767 -1032.1490 105 55 0 136.653106 \n", + "768 249.1307 32 203 1 329.846183 \n", + "\n", + "[769 rows x 10 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_history = customer_history.merge(customer_stats.churn_threshold, on=\"customer_id\")\n", + "customer_history" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "39a7fea9", + "metadata": {}, + "outputs": [], + "source": [ + "customer_history[\"churn\"] = customer_history[\"recency\"] > customer_history[\"churn_threshold\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "18b084cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "343\n", + "240\n" + ] + } + ], + "source": [ + "print(np.sum(customer_history[\"churn_90\"] == 1))\n", + "print(np.sum(customer_history[\"churn\"] == 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "68fd136a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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customer_idfirst_purchaselast_purchasemonetaryfrequencytotal_profitduration
0AA-103152014-03-312017-06-29505.7781825-362.88251186
1AA-103752014-04-212017-12-1170.4260009277.38241330
2AA-104802014-05-042017-04-15149.2093334435.82741077
3AA-106452014-06-222017-11-05282.6075006857.80331232
4AB-100152014-02-182016-11-10147.6926673129.3465996
........................
788XP-218652014-01-202017-11-1784.80921411621.23001397
789YC-218952014-11-172017-12-26681.79375051305.62901135
790YS-218802015-01-122017-12-21560.03700081778.29231074
791ZC-219102014-10-132017-11-06258.89377413-1032.14901120
792ZD-219252014-08-272017-06-11165.9937785249.13071019
\n", + "

793 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " customer_id first_purchase last_purchase monetary frequency \\\n", + "0 AA-10315 2014-03-31 2017-06-29 505.778182 5 \n", + "1 AA-10375 2014-04-21 2017-12-11 70.426000 9 \n", + "2 AA-10480 2014-05-04 2017-04-15 149.209333 4 \n", + "3 AA-10645 2014-06-22 2017-11-05 282.607500 6 \n", + "4 AB-10015 2014-02-18 2016-11-10 147.692667 3 \n", + ".. ... ... ... ... ... \n", + "788 XP-21865 2014-01-20 2017-11-17 84.809214 11 \n", + "789 YC-21895 2014-11-17 2017-12-26 681.793750 5 \n", + "790 YS-21880 2015-01-12 2017-12-21 560.037000 8 \n", + "791 ZC-21910 2014-10-13 2017-11-06 258.893774 13 \n", + "792 ZD-21925 2014-08-27 2017-06-11 165.993778 5 \n", + "\n", + " total_profit duration \n", + "0 -362.8825 1186 \n", + "1 277.3824 1330 \n", + "2 435.8274 1077 \n", + "3 857.8033 1232 \n", + "4 129.3465 996 \n", + ".. ... ... \n", + "788 621.2300 1397 \n", + "789 1305.6290 1135 \n", + "790 1778.2923 1074 \n", + "791 -1032.1490 1120 \n", + "792 249.1307 1019 \n", + "\n", + "[793 rows x 7 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_survival = (df.groupby(\"customer_id\").agg(\n", + " first_purchase=(\"order_date\",\"min\"),\n", + " last_purchase=(\"order_date\",\"max\"),\n", + " monetary=(\"revenue\",\"mean\"),\n", + " frequency=(\"order_id\",\"nunique\"),\n", + " total_profit=(\"profit\",\"sum\")\n", + ").reset_index())\n", + "customer_survival[\"duration\"] = (customer_survival[\"last_purchase\"] - customer_survival[\"first_purchase\"]).dt.days\n", + "customer_survival" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "83cb38da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['SF-20065', 'MA-17560', 'LC-16930', 'ES-14080', 'TB-21520',\n", + " 'KB-16600', 'CS-12400', 'PG-18895', 'RB-19705', 'PN-18775',\n", + " 'JM-15250', 'CV-12805', 'TS-21610', 'SH-19975', 'SG-20080',\n", + " 'VM-21685', 'FH-14365', 'LC-17140', 'LE-16810', 'JH-15985',\n", + " 'DB-13120', 'CC-12670', 'CA-12310', 'KH-16690', 'BB-10990',\n", + " 'AG-10495', 'JH-15910', 'DJ-13510', 'TD-20995', 'KW-16435',\n", + " 'AR-10405', 'MV-18190', 'SS-20140', 'RF-19840', 'KH-16510',\n", + " 'KC-16675', 'VB-21745', 'MP-17965', 'NF-18385', 'JM-15265',\n", + " 'KH-16630', 'BB-11545', 'EB-13705', 'LS-17245', 'BN-11515',\n", + " 'PH-18790', 'JC-15340', 'EP-13915', 'AS-10135', 'RC-19960',\n", + " 'GT-14710', 'LP-17080', 'FP-14320', 'EB-13840', 'JF-15415',\n", + " 'CS-11950', 'DV-13465', 'CK-12595', 'NG-18355', 'MO-17800',\n", + " 'BD-11605', 'JL-15835', 'TW-21025', 'CC-12430', 'RB-19570',\n", + " 'GT-14635', 'RA-19285', 'Dl-13600', 'AP-10915', 'KC-16540',\n", + " 'JF-15355', 'RF-19735', 'CP-12340', 'BF-11020', 'JC-16105',\n", + " 'RH-19495', 'JL-15175', 'GA-14725', 'HP-14815', 'GZ-14470',\n", + " 'EL-13735', 'GB-14530', 'DB-13060', 'PO-18865', 'BE-11335',\n", + " 'BM-11650', 'RK-19300', 'RA-19885', 'RB-19795', 'GH-14665',\n", + " 'SW-20275', 'LW-16990', 'PO-19195', 'AS-10630', 'PO-18850',\n", + " 'AT-10735', 'HR-14770', 'JH-15430', 'NS-18640', 'SN-20710',\n", + " 'BP-11185', 'CM-12160', 'FO-14305', 'DW-13540', 'RA-19915',\n", + " 'DL-13315', 'LH-16750', 'SP-20620', 'ZC-21910', 'DJ-13630',\n", + " 'JR-15700', 'CS-12355', 'PF-19225', 'BG-11695', 'EB-13975',\n", + " 'CS-12460', 'HA-14920', 'EB-14170', 'FG-14260', 'HM-14860',\n", + " 'AB-10600', 'SZ-20035', 'YS-21880', 'CK-12760', 'LT-17110',\n", + " 'MM-17920', 'PW-19030', 'DK-12835', 'KN-16390', 'KD-16345',\n", + " 'TS-21370', 'JW-15220', 'JD-15790', 'NG-18430', 'AR-10540',\n", + " 'LS-16975', 'JM-15535', 'AI-10855', 'MC-17845', 'JF-15490',\n", + " 'CL-11890', 'ME-17320', 'BO-11425', 'AB-10150', 'AH-10075',\n", + " 'MY-17380', 'ME-17725', 'JC-15385', 'CD-12280', 'MM-18055',\n", + " 'RD-19480', 'RD-19585', 'AW-10840', 'HF-14995', 'JK-15205',\n", + " 'DC-13285', 'AG-10675', 'MC-18100', 'JW-15955', 'VW-21775',\n", + " 'JE-15610', 'MR-17545', 'JA-15970', 'BT-11680', 'SL-20155',\n", + " 'AS-10045', 'GW-14605', 'FH-14275', 'BP-11230', 'JG-15160',\n", + " 'JP-15520', 'AS-10090', 'AC-10450', 'MD-17860', 'DB-13660',\n", + " 'EH-13765', 'NP-18325', 'SV-20365', 'CP-12085', 'AC-10615',\n", + " 'RW-19690', 'DB-13210', 'BD-11320', 'JS-16030', 'CM-12190',\n", + " 'GM-14500', 'CT-11995', 'TP-21130', 'KA-16525', 'DB-13270',\n", + " 'BF-11080', 'MM-17260', 'RD-19900', 'JB-15400', 'EH-14005',\n", + " 'CB-12415', 'AF-10870', 'NK-18490', 'HK-14890', 'IL-15100',\n", + " 'NS-18505', 'MH-17620', 'EA-14035', 'LA-16780', 'JH-15820',\n", + " 'QJ-19255', 'BF-11005', 'SM-20320', 'TP-21415', 'DK-13225',\n", + " 'MK-17905', 'JK-15625', 'FM-14215', 'SV-20935', 'EM-13825',\n", + " 'KN-16705', 'CC-12220', 'AM-10360', 'NP-18700', 'SB-20290',\n", + " 'MG-18145', 'ML-18040', 'MC-17590', 'CA-12265', 'SW-20455',\n", + " 'GM-14695', 'AH-10210', 'DB-12910', 'DL-13495', 'AB-10105',\n", + " 'BD-11635', 'SF-20200', 'DR-12880', 'RS-19765', 'JM-15655',\n", + " 'TA-21385', 'AS-10225', 'DG-13300', 'TC-21295', 'DB-13615',\n", + " 'JD-16015', 'GM-14440', 'SC-20680', 'SE-20110', 'SK-19990',\n", + " 'FM-14380', 'ML-17755', 'HG-15025', 'IG-15085', 'DL-13330',\n", + " 'DC-12850', 'RL-19615', 'XP-21865', 'SN-20560', 'VF-21715',\n", + " 'EM-13810', 'RB-19360', 'JK-15370', 'DL-12925', 'BP-11155',\n", + " 'TH-21550', 'MP-18175', 'EM-14140', 'AY-10555', 'JF-15565',\n", + " 'AR-10510', 'DV-13045', 'HL-15040', 'RD-19810', 'CR-12730',\n", + " 'JB-16000', 'AB-10060', 'KF-16285', 'NZ-18565', 'HH-15010',\n", + " 'KB-16405', 'JG-15310', 'EB-14110', 'SM-20950', 'PK-19075',\n", + " 'SH-20395', 'JE-16165', 'RW-19630', 'AH-10030', 'VG-21790',\n", + " 'SJ-20215', 'KM-16225', 'CM-12655', 'MW-18235', 'EB-13750',\n", + " 'GM-14455', 'PR-18880', 'AH-10195', 'NF-18595', 'BS-11755',\n", + " 'Dp-13240', 'LF-17185', 'AB-10165', 'RA-19945', 'JD-16150',\n", + " 'ND-18370', 'SF-20965', 'MG-17875', 'KB-16240', 'HW-14935',\n", + " 'SU-20665', 'CC-12550', 'TS-21505', 'MD-17350', 'RD-19660',\n", + " 'JK-15640', 'DK-13090', 'AG-10300', 'MH-18115', 'FM-14290',\n", + " 'SC-20770', 'CC-12610', 'AJ-10945', 'DB-13555', 'ML-17410',\n", + " 'AD-10180', 'MN-17935', 'EH-14125', 'TC-21535', 'BE-11410',\n", + " 'KL-16555', 'HG-14845', 'JE-15745', 'GT-14755', 'DM-13015',\n", + " 'AJ-10960', 'DS-13180', 'SC-20800', 'MM-18280', 'SC-20305',\n", + " 'PL-18925', 'CC-12475', 'JO-15145', 'SP-20920', 'DK-12895',\n", + " 'RB-19465', 'PV-18985', 'DD-13570', 'RB-19330', 'CB-12025',\n", + " 'JK-15730', 'CR-12820', 'AH-10585', 'WB-21850', 'LS-16945',\n", + " 'LO-17170', 'RS-19420', 'KT-16480', 'AT-10435', 'DW-13195',\n", + " 'MB-17305', 'TH-21235', 'SS-20875', 'GB-14575', 'MV-17485',\n", + " 'AG-10330', 'RR-19315', 'LH-16900', 'CD-11920', 'MC-17575',\n", + " 'DS-13030', 'MG-17680', 'BD-11620', 'PS-18970', 'SJ-20125',\n", + " 'AB-10255', 'NL-18310', 'MG-17650', 'RM-19675', 'SO-20335',\n", + " 'BD-11725', 'HA-14905', 'CK-12325', 'ES-14020', 'EB-13870',\n", + " 'PG-18820', 'CM-12385', 'EK-13795', 'MH-17455', 'SC-20050',\n", + " 'JO-15280', 'TT-21070', 'SC-20440', 'TB-21175', 'SC-20575',\n", + " 'AR-10345', 'MO-17500', 'LP-17095', 'AW-10930', 'HG-14965',\n", + " 'SW-20350', 'EG-13900', 'SC-20380', 'CG-12040', 'BD-11500',\n", + " 'JM-15580', 'MT-17815', 'LS-17200', 'CA-12775', 'JM-15865',\n", + " 'TM-21490', 'EJ-14155', 'CS-11845', 'BO-11350', 'NC-18415',\n", + " 'TM-21010', 'AJ-10780', 'NC-18535', 'ML-18265', 'DM-13345',\n", + " 'LC-16870', 'PJ-18835', 'FC-14245', 'YC-21895', 'CS-12250',\n", + " 'TH-21115', 'MS-17980', 'TB-21355', 'JK-16090', 'MG-17890',\n", + " 'JM-16195', 'LC-17050', 'VP-21730', 'BP-11095', 'BG-11035',\n", + " 'AJ-10795', 'AG-10765', 'AG-10900', 'KE-16420', 'DO-13645',\n", + " 'BP-11050', 'SS-20410', 'KD-16495', 'RB-19435', 'LR-17035',\n", + " 'RR-19525', 'EH-14185', 'DL-12865', 'AG-10390', 'FA-14230',\n", + " 'PC-18745', 'JR-16210', 'JF-15190', 'MW-18220', 'ME-18010',\n", + " 'DK-12985', 'NW-18400', 'GK-14620', 'AR-10825', 'AS-10285',\n", + " 'MZ-17335', 'SP-20860', 'BF-11275', 'CK-12205', 'AH-10120',\n", + " 'BW-11110', 'DR-12940', 'LC-16885', 'EC-14050', 'DK-13150',\n", + " 'AM-10705', 'RP-19390', 'OT-18730', 'TB-21055', 'RP-19855',\n", + " 'PJ-19015', 'DP-13105', 'VM-21835', 'SC-20695', 'AA-10375',\n", + " 'TC-21145', 'JP-16135', 'JC-15775', 'GD-14590', 'PS-18760',\n", + " 'CS-12505', 'CS-11860', 'MF-18250', 'HR-14830', 'JS-15595',\n", + " 'BN-11470', 'EM-13960', 'AP-10720', 'TB-21190', 'CC-12145',\n", + " 'DK-13375', 'PB-19105', 'GA-14515', 'CD-11980', 'GG-14650',\n", + " 'TT-21460', 'TG-21640', 'SR-20425', 'ED-13885', 'CM-12115',\n", + " 'MC-17275', 'ML-17395', 'GH-14410', 'BK-11260', 'LR-16915',\n", + " 'KH-16330', 'MY-18295', 'BM-11575', 'GH-14425', 'LT-16765',\n", + " 'JL-15235', 'MH-18025', 'AA-10645', 'BS-11380', 'MB-18085',\n", + " 'KH-16360', 'TC-21475', 'SG-20605', 'BF-11170', 'SV-20815',\n", + " 'MC-17605', 'PA-19060', 'LB-16735', 'CW-11905', 'CY-12745',\n", + " 'BH-11710', 'MO-17950', 'CA-11965', 'NM-18445', 'JE-15715',\n", + " 'JK-16120', 'PP-18955', 'EM-14200', 'RW-19540', 'JS-15685',\n", + " 'CR-12580', 'NP-18670', 'CM-11815', 'RD-19930', 'LW-16825',\n", + " 'EB-13930', 'DP-13000', 'CC-12370', 'DM-12955', 'PT-19090',\n", + " 'GZ-14545', 'BG-11740', 'EM-14065', 'JB-16045', 'HE-14800',\n", + " 'KN-16450', 'PB-19150', 'ST-20530', 'RD-19720', 'PM-19135',\n", + " 'EM-14095', 'PO-19180', 'HZ-14950', 'TZ-21580', 'TS-21430',\n", + " 'MJ-17740', 'DO-13435', 'BP-11290', 'SA-20830', 'SW-20245',\n", + " 'JL-15130', 'RO-19780', 'JP-15460', 'TN-21040', 'VP-21760',\n", + " 'NB-18655', 'BT-11530', 'JL-15505', 'SC-20230', 'RS-19870',\n", + " 'BF-10975', 'LW-17215', 'TB-21625', 'GM-14680', 'BS-11590',\n", + " 'PB-18805', 'BF-11215', 'HJ-14875', 'AZ-10750', 'MH-17785',\n", + " 'MS-17770', 'KL-16645', 'KM-16660', 'LB-16795', 'KM-16720',\n", + " 'KM-16375', 'MH-17290', 'SC-20845', 'CL-12700'], dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cutoff_date = pd.Timestamp(\"2017-06-30\")\n", + "future_customers = (df[df[\"order_date\"] > cutoff_date][\"customer_id\"].unique())\n", + "future_customers" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b4b63057", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "199\n" + ] + } + ], + "source": [ + "customer_survival[\"event\"] = (~customer_survival[\"customer_id\"].isin(future_customers)).astype(int)\n", + "print(customer_survival[customer_survival[\"event\"] == 1].shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5fae1c9e", + "metadata": {}, + "outputs": [], + "source": [ + "customer_survival = customer_survival.query(\"frequency > 3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5a06ccc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmf = KaplanMeierFitter()\n", + "kmf.fit(durations=customer_survival[\"duration\"], event_observed=customer_survival[\"event\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "eeb23e25", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,6))\n", + "kmf.plot_survival_function()\n", + "plt.title(\"Customer Survival Curve\")\n", + "plt.xlabel(\"Days\")\n", + "plt.ylabel(\"Probability Customer is Active\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "669d2646", + "metadata": {}, + "outputs": [], + "source": [ + "customer_survival[\"duration\"] = np.where(customer_survival[\"event\"] == 1,\n", + " (customer_survival[\"last_purchase\"] - customer_survival[\"first_purchase\"]).dt.days, (cutoff_date -customer_survival[\"first_purchase\"]).dt.days)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ad79c756", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "frequency\n", + "5 134\n", + "7 116\n", + "6 107\n", + "4 96\n", + "8 82\n", + "9 71\n", + "10 39\n", + "11 23\n", + "12 18\n", + "13 7\n", + "17 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_survival[\"frequency\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9b24558d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kmf = KaplanMeierFitter()\n", + "kmf.fit(durations=customer_survival[\"duration\"], event_observed=customer_survival[\"event\"])\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "kmf.plot_survival_function()\n", + "plt.title(\"Customer Survival Curve\")\n", + "plt.xlabel(\"Days since first purchase\")\n", + "plt.ylabel(\"Probability of Remaining Active\")\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.14.2.final.0)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6ad2b3221890273c857035d786c71cb7d3630772 Mon Sep 17 00:00:00 2001 From: Behniash Date: Sun, 9 Aug 2026 20:04:54 -0700 Subject: [PATCH 2/3] customer segmentation analysis and RFM --- notebooks/EDA.ipynb | 5 +- notebooks/Segmentation.ipynb | 8772 ++++++++++++++++++++++++++++++++++ 2 files changed, 8774 insertions(+), 3 deletions(-) create mode 100644 notebooks/Segmentation.ipynb diff --git a/notebooks/EDA.ipynb b/notebooks/EDA.ipynb index 138559b..406b303 100644 --- a/notebooks/EDA.ipynb +++ b/notebooks/EDA.ipynb @@ -13712,7 +13712,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "b59ff4d0", "metadata": {}, "outputs": [ @@ -13811,7 +13811,6 @@ } ], "source": [ - "\n", "fa = FactorAnalysis(n_components=5, random_state=42)\n", "X_fa = fa.fit_transform(X_standard)\n", "print(X_fa.shape)\n", @@ -13823,7 +13822,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv (3.14.2.final.0)", + "display_name": ".venv", "language": "python", "name": "python3" }, diff --git a/notebooks/Segmentation.ipynb b/notebooks/Segmentation.ipynb new file mode 100644 index 0000000..bc1277c --- /dev/null +++ b/notebooks/Segmentation.ipynb @@ -0,0 +1,8772 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1ee449be-36b4-4b00-b5c8-88624905af3f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import datetime as dt\n", + "\n", + "from sklearn.cluster import KMeans, DBSCAN \n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import silhouette_score, silhouette_samples\n", + "\n", + "import warnings; warnings.filterwarnings('ignore')\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "647990ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
01CA-2016-15215611/8/201611/11/2016Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.9136
12CA-2016-15215611/8/201611/11/2016Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs,...731.940030.00219.5820
23CA-2016-1386886/12/20166/16/2016Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036WestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters b...14.620020.006.8714
34US-2015-10896610/11/201510/18/2015Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.0310
45US-2015-10896610/11/201510/18/2015Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.368020.202.5164
..................................................................
99899990CA-2014-1104221/21/20141/23/2014Second ClassTB-21400Tom BoeckenhauerConsumerUnited StatesMiamiFlorida33180SouthFUR-FU-10001889FurnitureFurnishingsUltra Door Pull Handle25.248030.204.1028
99909991CA-2017-1212582/26/20173/3/2017Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestFUR-FU-10000747FurnitureFurnishingsTenex B1-RE Series Chair Mats for Low Pile Car...91.960020.0015.6332
99919992CA-2017-1212582/26/20173/3/2017Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestTEC-PH-10003645TechnologyPhonesAastra 57i VoIP phone258.576020.2019.3932
99929993CA-2017-1212582/26/20173/3/2017Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestOFF-PA-10004041Office SuppliesPaperIt's Hot Message Books with Stickers, 2 3/4\" x 5\"29.600040.0013.3200
99939994CA-2017-1199145/4/20175/9/2017Second ClassCC-12220Chris CortesConsumerUnited StatesWestminsterCalifornia92683WestOFF-AP-10002684Office SuppliesAppliancesAcco 7-Outlet Masterpiece Power Center, Wihtou...243.160020.0072.9480
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" + ], + "text/plain": [ + " Row ID Order ID Order Date Ship Date Ship Mode \\\n", + "0 1 CA-2016-152156 11/8/2016 11/11/2016 Second Class \n", + "1 2 CA-2016-152156 11/8/2016 11/11/2016 Second Class \n", + "2 3 CA-2016-138688 6/12/2016 6/16/2016 Second Class \n", + "3 4 US-2015-108966 10/11/2015 10/18/2015 Standard Class \n", + "4 5 US-2015-108966 10/11/2015 10/18/2015 Standard Class \n", + "... ... ... ... ... ... \n", + "9989 9990 CA-2014-110422 1/21/2014 1/23/2014 Second Class \n", + "9990 9991 CA-2017-121258 2/26/2017 3/3/2017 Standard Class \n", + "9991 9992 CA-2017-121258 2/26/2017 3/3/2017 Standard Class \n", + "9992 9993 CA-2017-121258 2/26/2017 3/3/2017 Standard Class \n", + "9993 9994 CA-2017-119914 5/4/2017 5/9/2017 Second Class \n", + "\n", + " Customer ID Customer Name Segment Country City \\\n", + "0 CG-12520 Claire Gute Consumer United States Henderson \n", + "1 CG-12520 Claire Gute Consumer United States Henderson \n", + "2 DV-13045 Darrin Van Huff Corporate United States Los Angeles \n", + "3 SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale \n", + "4 SO-20335 Sean O'Donnell Consumer United States Fort Lauderdale \n", + "... ... ... ... ... ... \n", + "9989 TB-21400 Tom Boeckenhauer Consumer United States Miami \n", + "9990 DB-13060 Dave Brooks Consumer United States Costa Mesa \n", + "9991 DB-13060 Dave Brooks Consumer United States Costa Mesa \n", + "9992 DB-13060 Dave Brooks Consumer United States Costa Mesa \n", + "9993 CC-12220 Chris Cortes Consumer United States Westminster \n", + "\n", + " State Postal Code Region Product ID Category \\\n", + "0 Kentucky 42420 South FUR-BO-10001798 Furniture \n", + "1 Kentucky 42420 South FUR-CH-10000454 Furniture \n", + "2 California 90036 West OFF-LA-10000240 Office Supplies \n", + "3 Florida 33311 South FUR-TA-10000577 Furniture \n", + "4 Florida 33311 South OFF-ST-10000760 Office Supplies \n", + "... ... ... ... ... ... \n", + "9989 Florida 33180 South FUR-FU-10001889 Furniture \n", + "9990 California 92627 West FUR-FU-10000747 Furniture \n", + "9991 California 92627 West TEC-PH-10003645 Technology \n", + "9992 California 92627 West OFF-PA-10004041 Office Supplies \n", + "9993 California 92683 West OFF-AP-10002684 Office Supplies \n", + "\n", + " Sub-Category Product Name \\\n", + "0 Bookcases Bush Somerset Collection Bookcase \n", + "1 Chairs Hon Deluxe Fabric Upholstered Stacking Chairs,... \n", + "2 Labels Self-Adhesive Address Labels for Typewriters b... \n", + "3 Tables Bretford CR4500 Series Slim Rectangular Table \n", + "4 Storage Eldon Fold 'N Roll Cart System \n", + "... ... ... \n", + "9989 Furnishings Ultra Door Pull Handle \n", + "9990 Furnishings Tenex B1-RE Series Chair Mats for Low Pile Car... \n", + "9991 Phones Aastra 57i VoIP phone \n", + "9992 Paper It's Hot Message Books with Stickers, 2 3/4\" x 5\" \n", + "9993 Appliances Acco 7-Outlet Masterpiece Power Center, Wihtou... \n", + "\n", + " Sales Quantity Discount Profit \n", + "0 261.9600 2 0.00 41.9136 \n", + "1 731.9400 3 0.00 219.5820 \n", + "2 14.6200 2 0.00 6.8714 \n", + "3 957.5775 5 0.45 -383.0310 \n", + "4 22.3680 2 0.20 2.5164 \n", + "... ... ... ... ... \n", + "9989 25.2480 3 0.20 4.1028 \n", + "9990 91.9600 2 0.00 15.6332 \n", + "9991 258.5760 2 0.20 19.3932 \n", + "9992 29.6000 4 0.00 13.3200 \n", + "9993 243.1600 2 0.00 72.9480 \n", + "\n", + "[9994 rows x 21 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('../datasets/superstore.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "75497c16", + "metadata": {}, + "outputs": [], + "source": [ + "df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "188677a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 9994 entries, 0 to 9993\n", + "Data columns (total 21 columns):\n", + " # 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frequencymonetaryrecency
customer_id
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AA-1037591056.39020
AA-1048041790.512260
AA-1064565086.93556
AB-100153886.156416
............
XP-21865112374.65844
YC-2189555454.3505
YS-2188086720.44410
ZC-21910138025.70755
ZD-2192551493.944203
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793 rows × 3 columns

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" + ], + "text/plain": [ + " frequency monetary recency\n", + "customer_id \n", + "AA-10315 5 5563.560 185\n", + "AA-10375 9 1056.390 20\n", + "AA-10480 4 1790.512 260\n", + "AA-10645 6 5086.935 56\n", + "AB-10015 3 886.156 416\n", + "... ... ... ...\n", + "XP-21865 11 2374.658 44\n", + "YC-21895 5 5454.350 5\n", + "YS-21880 8 6720.444 10\n", + "ZC-21910 13 8025.707 55\n", + "ZD-21925 5 1493.944 203\n", + "\n", + "[793 rows x 3 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "100c4fb4-f6c2-4a09-9815-ec0b4a838cb0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(17, 4))\n", + "for i, col in enumerate(rfm.columns):\n", + " sns.histplot(data=rfm, x=col, ax=ax[i], color=\"steelblue\", edgecolor=\"black\", bins=40)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d1dace2e-4a44-4eb1-8463-63a0c228cca9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "frequency 0.362409\n", + "monetary 2.476555\n", + "recency 2.276395\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm.skew()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "45e6f820-b301-4826-b10f-7058e176ca51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "frequency -1.039098\n", + "monetary -1.321134\n", + "recency -0.383143\n", + "dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_log = np.log(rfm)\n", + "rfm_log.skew()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e14415ff-7200-4320-9066-479de1033d98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5sspo3U8++STeeeedWGONNVxAAKBeffXVV3HDDTfkgbbp93n7ZdJSBilwlGYh1UQKSKX7+NWvfhVrr712HtD72muvxZAhQ3JwqVwKMu20007RvXv3PPj3ueeeiz//+c81/jsAQONX40DTK6+8khs0KQ/vnDlz8myjytI06DSaZb/99ltslRswYEAcf/zxsfPOO8eGG26Yg1mpM+jaa6+t6PBJo2tuv/32OOigg+Luu++OGTNmxHLLLRcPPPDAYqsHANA4/fvf/46xY8fmdk36vfII4LTeY0odc/PNN0v3AgDUu9QnkvplUkCp/Pd5s7ess846ce6559bo/tL6S6n9k7LEpABT6l9J6fDmXXtplVVWiVdffTWv3ZSWKkjrN3Xt2nWxPjYAoEQCTYcffnje0mjftCbSMcccE0vCxRdfHKeffnq8++67sfTSS+dZSq1atapSpl+/fjkP8ZgxY3LDKqXMS51DAAALc+utt+afxx57bBx33HFG5gIARSsNgElrI02ePDm3Xe66667Fcr9rrrlm3hYmZZbp06fPYvl7AEAJB5rK7b///rGkrbDCCrHFFlsstEwagZzS5QEALKqrrrrKRQMAGoSVVlppsQWZAADqJdCUpHzAt912W3zwwQcVi0GW23HHHeNnP/vZYqkcAMCSkkYIp9S7ab2mtD5TZWk9yNVWW82TAQAUhdRWue+++2LUqFF5CYHKUuq7M888s97qBgCUnkUONKV8vBtvvHHMnTs3r5uUpk9Xlo4BADQkI0aMiD322CO3bdI6BE2aNKlyfNasWfVWNwCAeR199NF5HcnevXvndaora9++vQsGABR3oCmN9E3TtEeOHDlfkAkAoCG67rrrYsiQIXHGGWfUd1UAABYqZZa56aab4qWXXsqDZAAA6lvT2jRo0ogZQSYAoLFI7Zttt922vqsBAPCD5syZk2cxCTIBAA12RtOWW24Zl112WXz//ffRvHmtlngCACgq22yzTTz++ON5rUkAKFZpveQOnbrUuHynjh1j7Muj67ROLHmtW7fOa0eOHTs2evTo4SkAYIm2MeqqfdGjZ6+YOGlSjct//fXX+TOxJrSJ6l7z2oz4TQGm7bbbLvbZZ5/5cgFvsskmORgFANBQrLnmmnHkkUfGxx9/HJttttl8M7dTm6dDhw71Vj8ASMrKymLgBXfX+GIMO3WAC9cIpY61LbbYInbeeec46qijomvXrlWOL7vssnHIIYfUW/0AaNxtjLpqX6Qg06K0cy4d1DeOvWJEjcpqExVhoGnUqFExbdq0/Pu111473/HUSSPQBAA0JPfcc0907NgxnnnmmbzNK7VtBJoAgGIwc+bMePTRR6NNmzZxxx13zHe8U6dOAk0AQHEHmo444oi8AQA0FnfddVd9VwEAoEbS4Jh3333X1QIAikbT+q4AAAAAAAAAJTKjaeTIkXmK9oJstdVW0bdv3x9bLwCAJeamm26KCRMmLPD4z3/+8+jSpeaLrwMA1GXqvMsuu2yBx1NKveOPP94TAAAUb6Dp/fffj/vvv3++hSjfeuutaNeuXbRt21agCQBoUJ5++ukYN25clX2TJk2Kjz76KLp16xZ77723QBMAUBRmzZo1X7/M3Llzczq9GTNmRP/+/QWaAIDiDjQddNBBeZvX22+/HT/96U9zRwwAQENyww03LHD/PffcExtssMESrxNAY9ajZ6+YOGlSjcpOnz69zusDDclKK62Us83M69tvv42DDz44dt9993qpFwBQuhY50LQg6667buy1115x3333xTHHHLO47hYAoN4ceeSRMXTo0Jg8eXLu1AFg8UhBpoEX3F2jspcOkpodaqJly5bxhz/8IU488cTchgEAWFKaLs47mz17du6IAQBoDMrKymLOnDnaNwBAg6BfBgBoEDOaUoq8V155Zb5cwK+++mpce+21MWLEiMVZPwCo03Q8nTp2jLEvj3aVS9zjjz8eU6ZMqbLvm2++iXvvvTe3c9Zee+16qxsAwLwp8h544IH5Lsrnn38el112Wey4444uGABQ3IGmhx9+OE4//fSqd9K8eay66qpx5ZVXRp8+fRZn/QCgTtPxDDt1gCtM/PGPf4wXX3yxypVo3bp1bLTRRrkjZ+mll3aVAICiMHXq1PlS4zVp0iRWXHHF2GWXXeL888+vt7oBAKVpkQNNxx13XN4AABqLRx55pL6rAABQI507d45p06a5WgBAww00AQAAAADQcNLGT58+vc7rA5SuWgeann322bj//vtjwoQJ0alTp9hpp51i5513Xry1AwBYQr7//vu47bbbYtSoUTFz5sxYa6214pBDDonVV1/dcwCwGDu6Ep1d8OOlNZn+9re/5bW0W7RoET169IiBAwfGMsss4/JCiViUtPGXDupb5/UBSletAk0nnnhiXH755bHppptG165d45133omLLroo9t9//7j11ltzbmAAgIbiyy+/jB122CHee++92GKLLWLZZZeNp59+Os4999y48cYb4+CDD67vKgI0mo6uRGcX/Pi0v/vuu2+0a9cuNt5445g9e3b84x//yOszPfHEE7Haaqu5xABA8Qaa0ijfv/71r7nzZeutt67YP27cuOjbt288+OCD0b9//8VdTwCAOnPhhRdG69at46OPPsoLaSeFQiGuueaavDblgAED8khhAIBicOyxx+ZBwEOGDIlmzZrlfV999VUeHHP66afHLbfcUt9VBABKSNNFPeH555+PffbZp0qQKdlwww3j8MMPj5EjRy7O+gEA1LnUvvnd735XEWRK0gztY445JlZYYYV48803PQsAQFGYMmVKfPrpp1WCTEmakX3mmWfqlwEAij/Q1LJly5g8eXK1x9L+Vq1aLY56AQAsMQtq38yZMyevI6J9AwAUi7QGU2qjpDUl56VfBgBoEIGmXXfdNR5//PE8FTuNoElpZdIClCnlzN///vf46U9/Wjc1BQCoI3vvvXf89re/jXvuuSd32sydOzdef/312G+//aJjx46xzjrruPYAQFFI6X7T2pKp/fLSSy/loNM333wTDz30UBx99NGx11571XcVAYASs8hrNHXu3Dnuvvvu+OUvfxnnnXdeNG3aNMrKyqJDhw4xbNiwnEIPAKAhOfLII+PDDz/M6xqkxbTL2ze9e/fOwScAgGLyt7/9LS9f0KtXr5zuNw0CTmn0Upvm97//fX1XDwAoMYscaEp23333eOedd2LMmDExYcKE6NSpU/To0SOPqgEAaGhSB00aQJMW1X7llVfyrKa11lorNtpoo/quGgBAtYOAH3744Xjrrbfy1qJFizzwt0uXLq4WANAwAk1JasSkUb5pAwBoDFZaaaXo27dvfVcDAKBGunXrljcAgAazRlPK9TtixIhqj40aNSr22WefxVUvAIA699VXX+VBM+lndc4+++y48cYbPRMAQFF48MEH45hjjqn2WFpjcrvttotPPvlkidcLAChtNQ40ffTRR/H4449Hv379qj2+2WabxcSJE3PACQCgIbjhhhti++23j2WXXbba47/4xS/i3HPPXeL1AgCozh//+Me8NlN10hpNAwYMiEsuucTFAwCKM9D04osvRs+ePfPi2Auy+eabx/PPP7+46gYAUKdGjhwZW2yxxQKPr7LKKjF79uz47LPPPBMAQL0qKyuLl19+OTbddNMFltEvAwAUdaApzVZaeeWVF1omHf/0008XR70AAOqc9g0A0FBMmTIlWrduHUsttdQCy+iXAQCKOtDUoUOHeOuttxZaJh3/oWAUAECx+KH2zZw5c+L999/XvgEA6l27du1i5syZOeC0IPplAICiDjT16dMnnnzyyXj22WerPf7GG2/EPffcE7vsssvirB8AQJ1J7Za0jsFXX31V7fHLLrssVl111ejcubNnAQCoV2kmU1pbckHrR6YBMkOHDl3g2toAAHWleU0LpplKJ510UvTt2zeOO+642HXXXaNLly4xadKkeOKJJ+Liiy+Ogw8+ONZbb706qywAwOJ02GGHxV/+8pfYcMMN47TTTotNNtkk2rRpEx988EEMGzYsD6J54IEHanXf3377bdx5553Rvn376N+//3zHR48eHW+//XYOZG299dbRpEmTxfCIAIDG7LzzzottttkmXnvttTjmmGNi7bXXzutJptsXXnhhHjxz/PHH13c1AYASU+NAU5JGzbRs2TIuuOCCuOiiiyr2t2jRIn71q1/FH//4x7qoYzz88MNx5ZVX5k6fnj175np07dq14viMGTPizDPPjMceeyyWWWaZ2H///ePEE0+Mpk1rPGELAChBqQ3z6KOPxlFHHZU7aypbZZVV4u67786Da2ojtUVuvvnm6NGjR5VAU1rI+9BDD42HHnoott1223jxxRfzQJ37778/WrVq9aMfEwDQeG222Wa5DXH00UfHPvvsU+XYT37yk9yeWH755eutfgBAaVqkQFMK3PzhD3+Ik08+OUaNGhVffPFFzhG8+eab59G/deGGG26oCGKlkTtvvvlm/OY3v4l//OMfFWX22muvmDZtWk5vM3Xq1DjyyCNz3eoq8AUANK51moYPHx6ffPJJjB07NmbNmpUHtGy66abRrFmzWt1nClA9//zzceCBB8brr79e5VgKPt17770xZsyYWGeddeLzzz/Pwag//elPMWTIkMX0qACAxmqHHXbIazG98soreUBu8+bNY4MNNog11lijvqsGAJSoRQo0lWvdunUeKVPXUrAoTflOM5hS2r4kpbbZe++9K8qktH2PP/54jBs3LjesktRhc8IJJ+QUOHUVAAMAGpeUEjhtP9aHH36Y2y9pptTll18+3/HbbrstdttttxxkKg90HXDAAXm/QBMAUBMp5W4aqJI2AID6VtS55f7973/nUcW/+MUvquyvPLo4BZpSp1B5kCnZfffdc47i5557bonWFwAobd9//30cdNBB8bvf/S7WX3/9astUHhxTLg2keeedd3L7pTppf0oVXHkDAAAAKAZFHWhKU8FXX331ePL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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(17, 4))\n", + "for i, col in enumerate(rfm_log.columns):\n", + " sns.histplot(data=rfm_log, x=col, ax=ax[i], color=\"steelblue\", edgecolor=\"black\", bins=40)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "07281848-8312-4c0f-b917-cf8b0386b848", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "rfm_scaled = scaler.fit_transform(rfm_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "92e5cdf6-66e0-4051-832d-af61446df3de", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " File \"d:\\data-analysis-notes\\data-analysis-notes\\.venv\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 247, in _count_physical_cores\n", + " cpu_count_physical = _count_physical_cores_win32()\n", + " File \"d:\\data-analysis-notes\\data-analysis-notes\\.venv\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 299, in _count_physical_cores_win32\n", + " cpu_info = subprocess.run(\n", + " \"wmic CPU Get NumberOfCores /Format:csv\".split(),\n", + " capture_output=True,\n", + " text=True,\n", + " )\n", + " File \"C:\\Python314\\Lib\\subprocess.py\", line 554, in run\n", + " with Popen(*popenargs, **kwargs) as process:\n", + " ~~~~~^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Python314\\Lib\\subprocess.py\", line 1038, in __init__\n", + " self._execute_child(args, executable, preexec_fn, close_fds,\n", + " ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " pass_fds, cwd, env,\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " ...<5 lines>...\n", + " gid, gids, uid, umask,\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " start_new_session, process_group)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Python314\\Lib\\subprocess.py\", line 1552, in _execute_child\n", + " hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n", + " ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\n", + " # no special security\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + " ...<4 lines>...\n", + " cwd,\n", + " ^^^^\n", + " startupinfo)\n", + " ^^^^^^^^^^^^\n" + ] + }, + { + "data": { + "text/plain": [ + "[2379.0000000000005,\n", + " 1497.810025903091,\n", + " 1184.8735885991837,\n", + " 986.8423242324138,\n", + " 862.3381967408704,\n", + " 768.2141486450515,\n", + " 706.8209462581372,\n", + " 650.2997591438732,\n", + " 598.3001054850829,\n", + " 561.3147997091394]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inertia=[]\n", + "for k in range(1,11):\n", + " model_km = KMeans(n_clusters=k, random_state=42)\n", + " model_km.fit(rfm_scaled)\n", + " inertia.append(model_km.inertia_)\n", + "inertia" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "13924e64-71ed-43d7-b557-3fa2da8b6a55", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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countmeanstdmin25%50%75%max
0250.00.3252430.1226740.0573530.2415980.3319340.4213000.545507
1272.00.2651060.156024-0.0722260.1452060.2838720.3916800.509600
290.00.1512390.168413-0.1995470.0053990.1586860.2977560.409157
3181.00.1877600.145000-0.0813640.0722560.1796700.3225120.442828
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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "0 250.0 0.325243 0.122674 0.057353 0.241598 0.331934 0.421300 0.545507\n", + "1 272.0 0.265106 0.156024 -0.072226 0.145206 0.283872 0.391680 0.509600\n", + "2 90.0 0.151239 0.168413 -0.199547 0.005399 0.158686 0.297756 0.409157\n", + "3 181.0 0.187760 0.145000 -0.081364 0.072256 0.179670 0.322512 0.442828" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "silhouette_scores_km = silhouette_samples(rfm_scaled, cat)\n", + "pd.Series(silhouette_scores_km).groupby(cat).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8b13fd5b-082a-49fd-9b5e-ae4eed17761d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.28978131, 1.01042898, 0.72283145],\n", + " [ 0.95296604, -0.56524536, -0.97663758],\n", + " [-0.76156993, -0.06482101, 0.98281843],\n", + " ...,\n", + " [ 0.70393937, 1.18959932, -1.50615735],\n", + " [ 1.73044029, 1.35793831, -0.20383967],\n", + " [-0.28978131, -0.23656146, 0.79376298]], shape=(793, 3))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "78e14c7c-97a0-4a17-92ef-be62afc71a8a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in np.unique(cat):\n", + " rfm_tmp = rfm_scaled[cat == i]\n", + " s = rfm_tmp[:, 1] * 12\n", + " s -= np.min(s)\n", + " plt.scatter(rfm_tmp[:, 0], rfm_tmp[:, 2], s=s, label=i)\n", + " plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "878617db", + "metadata": {}, + "outputs": [], + "source": [ + "rfm[\"KMenas_Cluster\"] = km_model.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ad842a26-dfc1-4b90-b805-d651490a89f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3], dtype=int32), array([250, 272, 90, 181]))" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(cat, return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "dd1954db-7343-4ed4-af75-bced0cef83f6", + "metadata": {}, + "outputs": [], + "source": [ + "rfm[\"f_score\"] = pd.qcut(rfm[\"frequency\"], q=7, labels=range(1,8))\n", + "rfm[\"m_score\"] = pd.qcut(rfm[\"monetary\"], q=7, labels=range(1,8))\n", + "rfm[\"r_score\"] = pd.qcut(rfm[\"recency\"], q=7, labels=range(7,0,-1))\n", + "rfm[\"scores\"] = (rfm[\"r_score\"].astype(int).astype(str) + rfm[\"f_score\"].astype(int).astype(str) + rfm[\"m_score\"].astype(int).astype(str))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a65c2885-e0b6-4a02-8bfe-47dde02fc065", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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frequencymonetaryrecencyKMenas_Clusterf_scorem_scorer_scorescores
customer_id
AA-1031555563.5601851272227
AA-1037591056.390203627762
AA-1048041790.5122601132213
AA-1064565086.935560365536
AB-100153886.1564162121112
...........................
XP-21865112374.658440745574
YC-2189555454.35053277727
YS-2188086720.444103577757
ZC-21910138025.707550775577
ZD-2192551493.9442031232223
\n", + "

793 rows × 8 columns

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" + ], + "text/plain": [ + " frequency monetary recency KMenas_Cluster f_score m_score \\\n", + "customer_id \n", + "AA-10315 5 5563.560 185 1 2 7 \n", + "AA-10375 9 1056.390 20 3 6 2 \n", + "AA-10480 4 1790.512 260 1 1 3 \n", + "AA-10645 6 5086.935 56 0 3 6 \n", + "AB-10015 3 886.156 416 2 1 2 \n", + "... ... ... ... ... ... ... \n", + "XP-21865 11 2374.658 44 0 7 4 \n", + "YC-21895 5 5454.350 5 3 2 7 \n", + "YS-21880 8 6720.444 10 3 5 7 \n", + "ZC-21910 13 8025.707 55 0 7 7 \n", + "ZD-21925 5 1493.944 203 1 2 3 \n", + "\n", + " r_score scores \n", + "customer_id \n", + "AA-10315 2 227 \n", + "AA-10375 7 762 \n", + "AA-10480 2 213 \n", + "AA-10645 5 536 \n", + "AB-10015 1 112 \n", + "... ... ... \n", + "XP-21865 5 574 \n", + "YC-21895 7 727 \n", + "YS-21880 7 757 \n", + "ZC-21910 5 577 \n", + "ZD-21925 2 223 \n", + "\n", + "[793 rows x 8 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d2a0bba7-05df-43fc-926a-568cba271546", + "metadata": {}, + "outputs": [], + "source": [ + "def segment(row):\n", + "\n", + " r = int(row[\"r_score\"])\n", + " f = int(row[\"f_score\"])\n", + " m = int(row[\"m_score\"])\n", + "\n", + " if r >= 6 and f >= 6 and m >= 6:\n", + " return \"Champions\"\n", + "\n", + " elif r >= 5 and f >= 5:\n", + " return \"Loyal Customers\"\n", + "\n", + " elif r >= 6 and f >= 3:\n", + " return \"Potential Loyalists\"\n", + "\n", + " elif r <= 2 and f >= 5 and m >= 5:\n", + " return \"At Risk\"\n", + "\n", + " elif r <= 2 and f <= 3 and m <= 3:\n", + " return \"Lost Customers\"\n", + "\n", + " else:\n", + " return \"Need Attention\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f3990dfe-b78e-496e-a10b-2e0d4e2262ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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frequencymonetaryrecencyKMenas_Clusterf_scorem_scorer_scorescoressegment
customer_id
AA-1031555563.5601851272227Need Attention
AA-1037591056.390203627762Loyal Customers
AA-1048041790.5122601132213Lost Customers
AA-1064565086.935560365536Need Attention
AB-100153886.1564162121112Lost Customers
..............................
XP-21865112374.658440745574Loyal Customers
YC-2189555454.35053277727Need Attention
YS-2188086720.444103577757Loyal Customers
ZC-21910138025.707550775577Loyal Customers
ZD-2192551493.9442031232223Lost Customers
\n", + "

793 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " frequency monetary recency KMenas_Cluster f_score m_score \\\n", + "customer_id \n", + "AA-10315 5 5563.560 185 1 2 7 \n", + "AA-10375 9 1056.390 20 3 6 2 \n", + "AA-10480 4 1790.512 260 1 1 3 \n", + "AA-10645 6 5086.935 56 0 3 6 \n", + "AB-10015 3 886.156 416 2 1 2 \n", + "... ... ... ... ... ... ... \n", + "XP-21865 11 2374.658 44 0 7 4 \n", + "YC-21895 5 5454.350 5 3 2 7 \n", + "YS-21880 8 6720.444 10 3 5 7 \n", + "ZC-21910 13 8025.707 55 0 7 7 \n", + "ZD-21925 5 1493.944 203 1 2 3 \n", + "\n", + " r_score scores segment \n", + "customer_id \n", + "AA-10315 2 227 Need Attention \n", + "AA-10375 7 762 Loyal Customers \n", + "AA-10480 2 213 Lost Customers \n", + "AA-10645 5 536 Need Attention \n", + "AB-10015 1 112 Lost Customers \n", + "... ... ... ... \n", + "XP-21865 5 574 Loyal Customers \n", + "YC-21895 7 727 Need Attention \n", + "YS-21880 7 757 Loyal Customers \n", + "ZC-21910 5 577 Loyal Customers \n", + "ZD-21925 2 223 Lost Customers \n", + "\n", + "[793 rows x 9 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm[\"segment\"] = rfm.apply(segment, axis=1)\n", + "rfm" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "29ccc296", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "segment\n", + "Need Attention 443\n", + "Lost Customers 118\n", + "Loyal Customers 102\n", + "Potential Loyalists 70\n", + "Champions 37\n", + "At Risk 23\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm[\"segment\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "cfbe4993", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sns.scatterplot(x=rfm[\"frequency\"], y=[\"recency\"], size=rfm[\"monetary\"], hue=rfm[\"segment\"])\n", + "sns.scatterplot(rfm, x='frequency', y='recency', size='monetary', hue='segment', alpha=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "74685e81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_id product_name \n", + "OFF-BI-10000545 GBC Ibimaster 500 Manual ProClick Binding System 43\n", + "FUR-CH-10002024 HON 5400 Series Task Chairs for Big and Tall 39\n", + "OFF-BI-10004728 Wilson Jones Turn Tabs Binder Tool for Ring Binders 38\n", + "FUR-TA-10002607 KI Conference Tables 38\n", + "OFF-LA-10001613 Avery File Folder Labels 35\n", + "OFF-FA-10002280 Advantus Plastic Paper Clips 34\n", + "OFF-ST-10002486 Eldon Shelf Savers Cubes and Bins 33\n", + "OFF-ST-10001526 Iceberg Mobile Mega Data/Printer Cart 33\n", + "FUR-FU-10003724 Westinghouse Clip-On Gooseneck Lamps 33\n", + "OFF-ST-10000675 File Shuttle II and Handi-File, Black 32\n", + "Name: quantity, dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# df[df[\"customer_id\"] == \"CG-12520\"]\n", + "\n", + "customer_g0 = list(rfm[rfm[\"KMenas_Cluster\"] == 0].index)\n", + "product_g0 = df.query('customer_id == @customer_g0').groupby([\"product_id\", \"product_name\"])[\"quantity\"].sum().sort_values(ascending=False)\n", + "product_g0[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "61267243", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_id product_name \n", + "TEC-MA-10002412 Cisco TelePresence System EX90 Videoconferencing Unit 22638.4800\n", + "TEC-CO-10004722 Canon imageCLASS 2200 Advanced Copier 12319.9648\n", + "OFF-SU-10000151 High Speed Automatic Electric Letter Opener 6550.1200\n", + "TEC-MA-10001127 HP Designjet T520 Inkjet Large Format Printer - 24\" Color 6124.9650\n", + "OFF-BI-10001120 Ibico EPK-21 Electric Binding System 5291.9720\n", + "TEC-MA-10001047 3D Systems Cube Printer, 2nd Generation, Magenta 5199.9600\n", + "TEC-MA-10000984 Okidata MB760 Printer 4476.8000\n", + "TEC-MA-10002927 Canon imageCLASS MF7460 Monochrome Digital Laser Multifunction Copier 3991.9800\n", + "OFF-BI-10003527 Fellowes PB500 Electric Punch Plastic Comb Binding Machine with Manual Bind 3914.6492\n", + "TEC-MA-10000045 Zebra ZM400 Thermal Label Printer 3482.8500\n", + "Name: revenue, dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_g0 = list(rfm[rfm[\"KMenas_Cluster\"] == 0].index)\n", + "product_g0 = df.query('customer_id == @customer_g0').groupby([\"product_id\", \"product_name\"])[\"revenue\"].mean().sort_values(ascending=False)\n", + "product_g0[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9ab03535", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_id\n", + "OFF-BI-10001922 32\n", + "OFF-BI-10003684 31\n", + "OFF-BI-10002026 30\n", + "OFF-ST-10000943 30\n", + "FUR-CH-10001146 28\n", + "TEC-AC-10002842 25\n", + "OFF-BI-10004970 25\n", + "OFF-AR-10004078 24\n", + "OFF-BI-10002982 24\n", + "OFF-PA-10000380 23\n", + "Name: quantity, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_g3 = list(rfm[rfm[\"KMenas_Cluster\"] == 3].index)\n", + "product_g3 = df.query('customer_id == @customer_g3').groupby([\"product_id\"])[\"quantity\"].sum().sort_values(ascending=False)\n", + "product_g3[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7b10e9b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_name\n", + "Canon PC1060 Personal Laser Copier 4899.930\n", + "Cubify CubeX 3D Printer Double Head Print 4799.984\n", + "GBC DocuBind P400 Electric Binding System 3810.772\n", + "Samsung Galaxy S4 Active 3499.930\n", + "Canon PC940 Copier 3149.930\n", + "Smead Adjustable Mobile File Trolley with Lockable Top 2934.330\n", + "Balt Solid Wood Round Tables 2678.940\n", + "Lexmark MX611dhe Monochrome Laser Printer 2549.985\n", + "Samsung Galaxy Mega 6.3 2435.942\n", + "GBC DocuBind 200 Manual Binding Machine 2357.488\n", + "Name: revenue, dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_g3 = list(rfm[rfm[\"KMenas_Cluster\"] == 3].index)\n", + "revenuewise_product_g3 = df.query('customer_id == @customer_g3').groupby([\"product_name\"])[\"revenue\"].mean().sort_values(ascending=False)\n", + "revenuewise_product_g3[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3a8a581c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_name\n", + "Avery Reinforcements for Hole-Punch Pages 2.574000\n", + "Avery 497 2.464000\n", + "Angle-D Ring Binders 2.461500\n", + "Storex Flexible Poly Binders with Double Pockets 2.376000\n", + "Maxell 4.7GB DVD+R 5/Pack 2.376000\n", + "Pressboard Covers with Storage Hooks, 9 1/2\" x 11\", Light Blue 2.291333\n", + "Heavy-Duty E-Z-D Binders 2.182000\n", + "Computer Printout Index Tabs 1.680000\n", + "Avery Durable Slant Ring Binders, No Labels 1.592000\n", + "Hoover Commercial Lightweight Upright Vacuum 1.392000\n", + "Name: revenue, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "revenuewise_product_g3[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "6b585338", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_id product_name customer_name \n", + "OFF-BI-10004995 GBC DocuBind P400 Electric Binding System Andy Reiter 2504.2216\n", + "TEC-CO-10003763 Canon PC1060 Personal Laser Copier Harry Marie 2302.9671\n", + "TEC-CO-10001766 Canon PC940 Copier Fred Hopkins 1480.4671\n", + "TEC-CO-10001449 Hewlett Packard LaserJet 3310 Copier Robert Marley 1007.9832\n", + "OFF-BI-10003925 Fellowes PB300 Plastic Comb Binding Machine Yana Sorensen 942.8157\n", + "TEC-PH-10000730 Samsung Galaxy S4 Active Kristen Hastings 909.9818\n", + "OFF-BI-10004390 GBC DocuBind 200 Manual Binding Machine Erica Smith 884.0580\n", + "OFF-BI-10001359 GBC DocuBind TL300 Electric Binding System Amy Cox 843.1706\n", + "OFF-ST-10002011 Smead Adjustable Mobile File Trolley with Lockable Top Yoseph Carroll 792.2691\n", + "TEC-AC-10003033 Plantronics CS510 - Over-the-Head monaural Wireless Headset System Rick Hansen 762.1845\n", + "Name: profit, dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_g3 = list(rfm[rfm[\"KMenas_Cluster\"] == 3].index)\n", + "profitwise_product_g3 = df.query('customer_id == @customer_g3').groupby([\"product_id\", \"product_name\", \"customer_name\"])[\"profit\"].mean().sort_values(ascending=False)\n", + "profitwise_product_g3[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e0e825da", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "product_id product_name customer_name \n", + "FUR-TA-10001889 Bush Advantage Collection Racetrack Conference Table Michael Moore -373.3048\n", + "FUR-TA-10000577 Bretford CR4500 Series Slim Rectangular Table Sean O'Donnell -383.0310\n", + "FUR-BO-10001972 O'Sullivan 4-Shelf Bookcase in Odessa Pine Juliana Krohn -384.7164\n", + "FUR-TA-10004289 BoxOffice By Design Rectangular and Half-Moon Meeting Room Tables Darrin Van Huff -420.0000\n", + "OFF-BI-10003650 GBC DocuBind 300 Electric Binding Machine Giulietta Weimer -462.8624\n", + "OFF-BI-10004584 GBC ProClick 150 Presentation Binding System Michael Kennedy -729.9138\n", + "OFF-AP-10002534 3.6 Cubic Foot Counter Height Office Refrigerator Joy Smith -766.0120\n", + "OFF-BI-10003527 Fellowes PB500 Electric Punch Plastic Comb Binding Machine with Manual Bind Nathan Cano -2287.7820\n", + "TEC-MA-10000822 Lexmark MX611dhe Monochrome Laser Printer Sharelle Roach -3399.9800\n", + "OFF-BI-10004995 GBC DocuBind P400 Electric Binding System Luke Foster -3701.8928\n", + "Name: profit, dtype: float64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "profitwise_product_g3[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5404f19c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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frequencymonetaryrecencyKMenas_Clusterf_scorem_scorer_scorescoressegmentAOV
customer_id
AA-1031555563.5601851272227Need Attention1112.712000
AA-1037591056.390203627762Loyal Customers117.376667
AA-1048041790.5122601132213Lost Customers447.628000
AA-1064565086.935560365536Need Attention847.822500
AB-100153886.1564162121112Lost Customers295.385333
.................................
XP-21865112374.658440745574Loyal Customers215.878000
YC-2189555454.35053277727Need Attention1090.870000
YS-2188086720.444103577757Loyal Customers840.055500
ZC-21910138025.707550775577Loyal Customers617.362077
ZD-2192551493.9442031232223Lost Customers298.788800
\n", + "

793 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " frequency monetary recency KMenas_Cluster f_score m_score \\\n", + "customer_id \n", + "AA-10315 5 5563.560 185 1 2 7 \n", + "AA-10375 9 1056.390 20 3 6 2 \n", + "AA-10480 4 1790.512 260 1 1 3 \n", + "AA-10645 6 5086.935 56 0 3 6 \n", + "AB-10015 3 886.156 416 2 1 2 \n", + "... ... ... ... ... ... ... \n", + "XP-21865 11 2374.658 44 0 7 4 \n", + "YC-21895 5 5454.350 5 3 2 7 \n", + "YS-21880 8 6720.444 10 3 5 7 \n", + "ZC-21910 13 8025.707 55 0 7 7 \n", + "ZD-21925 5 1493.944 203 1 2 3 \n", + "\n", + " r_score scores segment AOV \n", + "customer_id \n", + "AA-10315 2 227 Need Attention 1112.712000 \n", + "AA-10375 7 762 Loyal Customers 117.376667 \n", + "AA-10480 2 213 Lost Customers 447.628000 \n", + "AA-10645 5 536 Need Attention 847.822500 \n", + "AB-10015 1 112 Lost Customers 295.385333 \n", + "... ... ... ... ... \n", + "XP-21865 5 574 Loyal Customers 215.878000 \n", + "YC-21895 7 727 Need Attention 1090.870000 \n", + "YS-21880 7 757 Loyal Customers 840.055500 \n", + "ZC-21910 5 577 Loyal Customers 617.362077 \n", + "ZD-21925 2 223 Lost Customers 298.788800 \n", + "\n", + "[793 rows x 10 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm[\"AOV\"] = rfm[\"monetary\"] / rfm[\"frequency\"] # revenue / order count\n", + "rfm" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2993e44b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer_id\n", + "AA-10315 11\n", + "AA-10375 15\n", + "AA-10480 12\n", + "AA-10645 18\n", + "AB-10015 6\n", + " ..\n", + "XP-21865 28\n", + "YC-21895 8\n", + "YS-21880 11\n", + "ZC-21910 31\n", + "ZD-21925 9\n", + "Name: product_id, Length: 793, dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "product_diversity = (df.groupby(\"customer_id\")[\"product_id\"].nunique())\n", + "product_diversity" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "a1291ede", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer_id\n", + "AA-10315 3\n", + "AA-10375 3\n", + "AA-10480 3\n", + "AA-10645 3\n", + "AB-10015 3\n", + " ..\n", + "XP-21865 3\n", + "YC-21895 3\n", + "YS-21880 3\n", + "ZC-21910 3\n", + "ZD-21925 3\n", + "Name: category, Length: 793, dtype: int64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "category_diversity = (df.groupby(\"customer_id\")[\"category\"].nunique())\n", + "category_diversity" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b9eee168", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"cohort_month\"] = (df.groupby(\"customer_id\")[\"order_date\"].transform(\"min\").dt.to_period(\"M\"))\n", + "df[\"cohort_index\"] = ((df[\"order_month\"].dt.year - df[\"cohort_month\"].dt.year) * 12 +(df[\"order_month\"].dt.month - df[\"cohort_month\"].dt.month))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "f48b916c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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row_idorder_idorder_dateship_dateship_modecustomer_idcustomer_namesegmentcountrycitystatepostal_coderegionproduct_idcategorysub-categoryproduct_namerevenuequantitydiscountprofitorder_yearorder_monthorder_month_nameorder_daycohort_monthcohort_index
01CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.913620162016-11NovemberTuesday2015-1013
12CA-2016-1521562016-11-082016-11-11Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs,...731.940030.00219.582020162016-11NovemberTuesday2015-1013
23CA-2016-1386882016-06-122016-06-16Second ClassDV-13045Darrin Van HuffCorporateUnited StatesLos AngelesCalifornia90036WestOFF-LA-10000240Office SuppliesLabelsSelf-Adhesive Address Labels for Typewriters b...14.620020.006.871420162016-06JuneSunday2016-060
34US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.031020152015-10OctoberSunday2015-100
45US-2015-1089662015-10-112015-10-18Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthOFF-ST-10000760Office SuppliesStorageEldon Fold 'N Roll Cart System22.368020.202.516420152015-10OctoberSunday2015-100
56CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Access...48.860070.0014.169420142014-06JuneMonday2014-060
67CA-2014-1158122014-06-092014-06-14Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestOFF-AR-10002833Office SuppliesArtNewell 3227.280040.001.965620142014-06JuneMonday2014-060
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2014-0849.08.03.011.05.03.04.02.04.03.05.06.02.09.06.08.013.02.01.04.03.07.06.07.09.015.05.011.011.02.04.03.08.07.07.09.08.09.012.011.010.0NaNNaNNaNNaNNaNNaNNaN
2014-0968.09.09.011.01.02.09.010.09.02.06.08.012.06.013.015.02.05.09.05.09.08.07.04.012.03.015.013.03.02.09.09.08.011.013.09.015.010.016.012.0NaNNaNNaNNaNNaNNaNNaNNaN
2014-1042.03.05.01.03.04.05.04.03.04.04.07.04.08.05.02.03.04.07.09.05.04.05.010.07.05.07.03.04.06.05.07.010.08.04.011.07.013.014.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
2014-1162.015.0NaN3.05.04.02.06.03.05.011.05.013.015.02.04.05.08.03.06.07.05.016.06.010.014.04.02.013.012.011.07.05.09.014.012.019.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2014-1249.03.04.04.02.03.04.06.05.09.06.06.09.04.01.05.06.05.06.06.07.012.04.012.012.04.03.04.05.08.07.07.09.010.09.015.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-017.0NaN1.0NaN1.01.02.0NaNNaN1.01.01.01.0NaNNaN1.01.0NaNNaN1.0NaN1.02.0NaNNaN1.02.02.02.01.01.01.0NaN2.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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2015-0412.0NaNNaN1.0NaN1.03.02.0NaNNaN1.01.0NaNNaN1.03.01.06.01.02.02.02.0NaNNaN1.0NaN2.03.01.04.04.01.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-0512.0NaNNaN1.02.0NaN2.02.01.01.03.02.01.02.01.03.03.02.02.01.01.0NaN3.01.0NaN3.01.0NaN6.02.03.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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2015-078.0NaNNaN2.03.01.0NaN2.0NaNNaNNaN1.01.0NaNNaN1.02.01.01.0NaN1.01.01.02.0NaN2.02.0NaN3.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-0812.03.01.02.03.0NaNNaNNaNNaN1.02.0NaNNaN4.02.03.03.0NaN1.02.05.0NaN2.03.01.05.02.03.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-0916.01.03.01.04.01.03.03.01.0NaN1.07.03.02.03.02.0NaN2.04.05.04.03.0NaN4.07.02.04.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-109.0NaN4.0NaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.01.02.01.01.03.0NaNNaN2.01.02.01.01.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-1110.01.01.0NaNNaNNaN1.01.02.01.01.02.01.02.0NaN2.03.0NaNNaNNaNNaN2.04.0NaN5.05.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2015-1213.01.0NaN1.02.02.0NaN3.01.03.02.02.01.01.0NaN3.02.0NaN1.02.02.01.01.04.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-016.01.0NaN1.0NaN1.01.01.0NaN1.01.01.0NaN1.02.0NaN2.0NaN2.02.02.0NaN1.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-022.0NaN1.0NaNNaNNaNNaNNaN1.01.0NaN1.0NaN1.0NaNNaNNaNNaN1.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-036.0NaN3.0NaN3.0NaN1.01.01.01.03.01.01.02.02.01.0NaN1.01.01.04.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-047.02.0NaN1.01.04.0NaN1.0NaN2.0NaN1.01.0NaNNaN2.0NaN2.01.01.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-057.0NaN1.02.01.01.02.01.02.01.02.01.01.02.0NaNNaN2.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2016-068.01.02.03.01.01.01.01.0NaN2.0NaNNaN2.0NaN2.03.0NaN1.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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4.0 3.0 14.0 3.0 11.0 5.0 7.0 8.0 15.0 \n", + "2014-05 4.0 3.0 3.0 5.0 4.0 11.0 7.0 6.0 16.0 7.0 \n", + "2014-06 3.0 4.0 6.0 9.0 9.0 5.0 3.0 13.0 7.0 14.0 \n", + "2014-07 4.0 6.0 7.0 9.0 4.0 6.0 11.0 10.0 13.0 9.0 \n", + "2014-08 8.0 7.0 7.0 9.0 8.0 9.0 12.0 11.0 10.0 NaN \n", + "2014-09 8.0 11.0 13.0 9.0 15.0 10.0 16.0 12.0 NaN NaN \n", + "2014-10 10.0 8.0 4.0 11.0 7.0 13.0 14.0 NaN NaN NaN \n", + "2014-11 5.0 9.0 14.0 12.0 19.0 22.0 NaN NaN NaN NaN \n", + "2014-12 9.0 10.0 9.0 15.0 15.0 NaN NaN NaN NaN NaN \n", + "2015-01 NaN 2.0 NaN 1.0 NaN NaN NaN NaN NaN NaN \n", + "2015-02 2.0 2.0 2.0 NaN NaN NaN NaN NaN NaN NaN \n", + "2015-03 5.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-04 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-08 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-09 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-10 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-11 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2015-12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-02 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-04 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-08 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-10 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-11 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2016-12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-04 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-09 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-10 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2017-11 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "cohort_index 42 43 44 45 46 47 \n", + "cohort_month \n", + "2014-01 1.0 2.0 9.0 9.0 10.0 6.0 \n", + "2014-02 3.0 4.0 6.0 6.0 3.0 NaN \n", + "2014-03 19.0 8.0 18.0 16.0 NaN NaN \n", + "2014-04 8.0 14.0 14.0 NaN NaN NaN \n", + "2014-05 17.0 9.0 NaN NaN NaN NaN \n", + "2014-06 11.0 NaN NaN NaN NaN NaN \n", + "2014-07 NaN NaN NaN NaN NaN NaN \n", + "2014-08 NaN NaN NaN NaN NaN NaN \n", + "2014-09 NaN NaN NaN NaN NaN NaN \n", + "2014-10 NaN NaN NaN NaN NaN NaN \n", + "2014-11 NaN NaN NaN NaN NaN NaN \n", + "2014-12 NaN NaN NaN NaN NaN NaN \n", + "2015-01 NaN NaN NaN NaN NaN NaN \n", + "2015-02 NaN NaN NaN NaN NaN NaN \n", + "2015-03 NaN NaN NaN NaN NaN NaN \n", + "2015-04 NaN NaN NaN NaN NaN NaN \n", + "2015-05 NaN NaN NaN NaN NaN NaN \n", + "2015-06 NaN NaN NaN NaN NaN NaN \n", + 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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cohort_table = cohort_data.pivot_table(index=\"cohort_month\", columns=\"cohort_index\", values=\"customer_id\")\n", + "cohort_table " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "62fa846e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NaN NaN \n", + "2016-02 NaN NaN NaN NaN NaN \n", + "2016-03 NaN NaN NaN NaN NaN \n", + "2016-04 NaN NaN NaN NaN NaN \n", + "2016-05 NaN NaN NaN NaN NaN \n", + "2016-06 NaN NaN NaN NaN NaN \n", + "2016-07 NaN NaN NaN NaN NaN \n", + "2016-08 NaN NaN NaN NaN NaN \n", + "2016-10 NaN NaN NaN NaN NaN \n", + "2016-11 NaN NaN NaN NaN NaN \n", + "2016-12 NaN NaN NaN NaN NaN \n", + "2017-03 NaN NaN NaN NaN NaN \n", + "2017-04 NaN NaN NaN NaN NaN \n", + "2017-06 NaN NaN NaN NaN NaN \n", + "2017-07 NaN NaN NaN NaN NaN \n", + "2017-09 NaN NaN NaN NaN NaN \n", + "2017-10 NaN NaN NaN NaN NaN \n", + "2017-11 NaN NaN NaN NaN NaN \n", + "\n", + "cohort_index 41 42 43 44 45 46 \\\n", + "cohort_month \n", + "2014-01 12.500000 3.125000 6.250000 28.125000 28.125000 31.25 \n", + "2014-02 12.500000 12.500000 16.666667 25.000000 25.000000 12.50 \n", + "2014-03 9.230769 29.230769 12.307692 27.692308 24.615385 NaN \n", + "2014-04 26.785714 14.285714 25.000000 25.000000 NaN NaN \n", + "2014-05 12.500000 30.357143 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+ "2015-10 NaN \n", + "2015-11 NaN \n", + "2015-12 NaN \n", + "2016-01 NaN \n", + "2016-02 NaN \n", + "2016-03 NaN \n", + "2016-04 NaN \n", + "2016-05 NaN \n", + "2016-06 NaN \n", + "2016-07 NaN \n", + "2016-08 NaN \n", + "2016-10 NaN \n", + "2016-11 NaN \n", + "2016-12 NaN \n", + "2017-03 NaN \n", + "2017-04 NaN \n", + "2017-06 NaN \n", + "2017-07 NaN \n", + "2017-09 NaN \n", + "2017-10 NaN \n", + "2017-11 NaN " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retention = cohort_table.divide(cohort_table.iloc[:,0], axis=0)\n", + "retention * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "39fab339", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dbscan = DBSCAN(eps=0.9, min_samples=1)\n", + "db = dbscan.fit_predict(rfm_scaled)\n", + "np.unique(dbscan.labels_)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "47f7aa69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2382888477884902" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "silhouette = silhouette_score(rfm_scaled, dbscan.labels_)\n", + "silhouette" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "bf945b78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% \\\n", + "0 776.0 0.235767 0.266377 -0.747950 0.142842 0.331626 0.431388 \n", + "1 4.0 0.484547 0.181421 0.219456 0.456336 0.544399 0.572610 \n", + "2 2.0 0.789295 0.013148 0.779997 0.784646 0.789295 0.793943 \n", + "3 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "4 4.0 0.622753 0.195803 0.330683 0.603260 0.707873 0.727366 \n", + "5 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "6 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "7 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "8 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "9 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "10 1.0 0.000000 NaN 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " max \n", + "0 0.509668 \n", + "1 0.629932 \n", + "2 0.798592 \n", + "3 0.000000 \n", + "4 0.744582 \n", + "5 0.000000 \n", + "6 0.000000 \n", + "7 0.000000 \n", + "8 0.000000 \n", + "9 0.000000 \n", + "10 0.000000 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "silhouette_scores = silhouette_samples(rfm_scaled, dbscan.labels_)\n", + "pd.Series(silhouette_scores).groupby(dbscan.labels_).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "943fc1cf", + "metadata": {}, + "outputs": [], + "source": [ + "rfm[\"dbscan\"] = dbscan.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "973ebe2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([-1, 1]), array([ 40, 753]))" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.neighbors import LocalOutlierFactor\n", + "\n", + "lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05, n_jobs=-1)\n", + "pred = lof.fit_predict(rfm_scaled)\n", + "np.unique(pred, return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "1e1caf90", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16, 8))\n", + "\n", + "clusters = [3, 4, 5, 7, 10]\n", + "inertia = {c: [] for c in clusters}\n", + "span = np.linspace(0.01, 0.5, 50)\n", + "for s in span:\n", + " pred = LocalOutlierFactor(n_neighbors=20, contamination=s, n_jobs=-1).fit_predict(rfm_scaled)\n", + " for cluster in clusters:\n", + " inertia[cluster].append(KMeans(n_clusters=cluster, init=\"k-means++\").fit(rfm_scaled[pred == 1]).inertia_)\n", + "\n", + "for cluster, inertia_ in inertia.items():\n", + " plt.plot(span, inertia_, marker='o', ms=4, label=cluster)\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a9e77292457c5d8e6f387b44cc4f9db37121d80e Mon Sep 17 00:00:00 2001 From: Behniash Date: Sun, 9 Aug 2026 20:34:52 -0700 Subject: [PATCH 3/3] Feature Importance and extraction --- notebooks/EDA.ipynb | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/notebooks/EDA.ipynb b/notebooks/EDA.ipynb index 406b303..4c22227 100644 --- a/notebooks/EDA.ipynb +++ b/notebooks/EDA.ipynb @@ -13513,7 +13513,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "7d8bf3a5", "metadata": {}, "outputs": [ @@ -13538,9 +13538,7 @@ "# Add target for coloring\n", "pca_3d_df[\"diagnosis\"] = df[\"diagnosis\"].values\n", "\n", - "\n", "print(\"Explained variance:\", pca_3d.explained_variance_ratio_)\n", - "\n", "print(\"Total variance:\", pca_3d.explained_variance_ratio_.sum())" ] }, @@ -13576,7 +13574,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "1313a967", "metadata": {}, "outputs": [ @@ -13592,7 +13590,6 @@ } ], "source": [ - "\n", "lda = LinearDiscriminantAnalysis(n_components=1)\n", "X_lda = lda.fit_transform(X_standard, df[\"diagnosis\"])\n", "X_lda.shape" @@ -13681,7 +13678,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "728c89dd", "metadata": {}, "outputs": [ @@ -13699,7 +13696,6 @@ "source": [ "plt.figure(figsize=(10,5))\n", "\n", - "\n", "for label in sorted(lda_df[\"diagnosis\"].unique()):\n", " subset = lda_df[lda_df[\"diagnosis\"] == label]\n", " plt.hist(subset[\"LD1\"], bins=30, alpha=0.5, label=f\"Diagnosis {label}\")\n",