{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DatasetData TypesDefault TaskTarget Variable# Instances# AttributesMissing Values
0cancerMultivariateClassificationClass68310N
1creditMultivariateClassificationdefault2400024N
2employeeMultivariateClassificationleft1499910N
3heartMultivariateClassificationDEATH20017N
4hepatitisMultivariateClassificationClass15432Y
5incomeMultivariateClassificationincome >50K3256114Y
6irisMultivariateClassificationClass1005N
7juiceMultivariateClassificationPurchase107015N
8heart_diseaseMultivariateClassificationDisease27014N
9glassMultivariateMulticlass ClassificationType21410N
10satelliteMultivariateMulticlass ClassificationClass643537N
11pokerMultivariateMulticlass ClassificationCLASS10000011N
12automobileMultivariateRegressionprice20226Y
13bikeMultivariateRegressioncnt1737915N
14bostonMultivariateRegressionmedv50614N
15diamondMultivariateRegressionPrice60008N
16goldMultivariateRegressionGold_T+222558121N
17tweetsTextNLPtweet85942N
18amazonTextNLP / ClassificationreviewText200002N
19kivaTextNLP / Classificationen68187N
20spxTextNLP / Regressiontext8744N
21germanyMultivariateAssociation Rule MiningInvoiceNo, Description94958N
22franceMultivariateAssociation Rule MiningInvoiceNo, Description85578N
\n", "
" ], "text/plain": [ " Dataset Data Types Default Task \\\n", "0 cancer Multivariate Classification \n", "1 credit Multivariate Classification \n", "2 employee Multivariate Classification \n", "3 heart Multivariate Classification \n", "4 hepatitis Multivariate Classification \n", "5 income Multivariate Classification \n", "6 iris Multivariate Classification \n", "7 juice Multivariate Classification \n", "8 heart_disease Multivariate Classification \n", "9 glass Multivariate Multiclass Classification \n", "10 satellite Multivariate Multiclass Classification \n", "11 poker Multivariate Multiclass Classification \n", "12 automobile Multivariate Regression \n", "13 bike Multivariate Regression \n", "14 boston Multivariate Regression \n", "15 diamond Multivariate Regression \n", "16 gold Multivariate Regression \n", "17 tweets Text NLP \n", "18 amazon Text NLP / Classification \n", "19 kiva Text NLP / Classification \n", "20 spx Text NLP / Regression \n", "21 germany Multivariate Association Rule Mining \n", "22 france Multivariate Association Rule Mining \n", "\n", " Target Variable # Instances # Attributes Missing Values \n", "0 Class 683 10 N \n", "1 default 24000 24 N \n", "2 left 14999 10 N \n", "3 DEATH 200 17 N \n", "4 Class 154 32 Y \n", "5 income >50K 32561 14 Y \n", "6 Class 100 5 N \n", "7 Purchase 1070 15 N \n", "8 Disease 270 14 N \n", "9 Type 214 10 N \n", "10 Class 6435 37 N \n", "11 CLASS 100000 11 N \n", "12 price 202 26 Y \n", "13 cnt 17379 15 N \n", "14 medv 506 14 N \n", "15 Price 6000 8 N \n", "16 Gold_T+22 2558 121 N \n", "17 tweet 8594 2 N \n", "18 reviewText 20000 2 N \n", "19 en 6818 7 N \n", "20 text 874 4 N \n", "21 InvoiceNo, Description 9495 8 N \n", "22 InvoiceNo, Description 8557 8 N " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.datasets import get_data\n", "get_data('index')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053637022728ALARM CLOCK BAKELIKE PINK2412/1/2010 8:453.7512583.0France
153637022727ALARM CLOCK BAKELIKE RED2412/1/2010 8:453.7512583.0France
253637022726ALARM CLOCK BAKELIKE GREEN1212/1/2010 8:453.7512583.0France
353637021724PANDA AND BUNNIES STICKER SHEET1212/1/2010 8:450.8512583.0France
453637021883STARS GIFT TAPE2412/1/2010 8:450.6512583.0France
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" ], "text/plain": [ " InvoiceNo StockCode Description Quantity \\\n", "0 536370 22728 ALARM CLOCK BAKELIKE PINK 24 \n", "1 536370 22727 ALARM CLOCK BAKELIKE RED 24 \n", "2 536370 22726 ALARM CLOCK BAKELIKE GREEN 12 \n", "3 536370 21724 PANDA AND BUNNIES STICKER SHEET 12 \n", "4 536370 21883 STARS GIFT TAPE 24 \n", "\n", " InvoiceDate UnitPrice CustomerID Country \n", "0 12/1/2010 8:45 3.75 12583.0 France \n", "1 12/1/2010 8:45 3.75 12583.0 France \n", "2 12/1/2010 8:45 3.75 12583.0 France \n", "3 12/1/2010 8:45 0.85 12583.0 France \n", "4 12/1/2010 8:45 0.65 12583.0 France " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = get_data('france')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from pycaret.arules import *" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Description Value
session_id1262
# Transactions461
# Items1565
Ignore ItemsNone
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s = setup(data, 'InvoiceNo', item_id='Description')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "a1 = create_model()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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antecedentsconsequentsantecedent supportconsequent supportsupportconfidenceliftleverageconviction
0(JUMBO BAG WOODLAND ANIMALS)(POSTAGE)0.06510.67460.06511.00001.48230.0212inf
1(SET/6 RED SPOTTY PAPER PLATES, SET/20 RED RET...(SET/6 RED SPOTTY PAPER CUPS)0.08680.11710.08460.97508.32360.074435.3145
2(SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO...(SET/6 RED SPOTTY PAPER PLATES)0.08680.10850.08460.97508.98950.075235.6616
3(SET/6 RED SPOTTY PAPER PLATES, POSTAGE, SET/2...(SET/6 RED SPOTTY PAPER CUPS)0.07160.11710.06940.96978.27830.061029.1345
4(POSTAGE, SET/6 RED SPOTTY PAPER CUPS, SET/20 ...(SET/6 RED SPOTTY PAPER PLATES)0.07160.10850.06940.96978.94060.061729.4208
..............................
136(STRAWBERRY LUNCH BOX WITH CUTLERY)(LUNCH BOX WITH CUTLERY RETROSPOT )0.10410.12360.05420.52084.21240.04141.8289
137(LUNCH BAG APPLE DESIGN)(LUNCH BAG SPACEBOY DESIGN )0.10850.10410.05640.52004.99420.04511.8664
138(LUNCH BAG APPLE DESIGN)(LUNCH BAG RED RETROSPOT)0.10850.13230.05640.52003.92980.04201.8077
139(PLASTERS IN TIN CIRCUS PARADE , POSTAGE)(PLASTERS IN TIN SPACEBOY)0.12580.11930.06290.50004.19090.04791.7614
140(LUNCH BAG RED RETROSPOT, POSTAGE)(LUNCH BAG APPLE DESIGN)0.10410.10850.05210.50004.61000.04081.7831
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141 rows × 9 columns

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" ], "text/plain": [ " antecedents \\\n", "0 (JUMBO BAG WOODLAND ANIMALS) \n", "1 (SET/6 RED SPOTTY PAPER PLATES, SET/20 RED RET... \n", "2 (SET/6 RED SPOTTY PAPER CUPS, SET/20 RED RETRO... \n", "3 (SET/6 RED SPOTTY PAPER PLATES, POSTAGE, SET/2... \n", "4 (POSTAGE, SET/6 RED SPOTTY PAPER CUPS, SET/20 ... \n", ".. ... \n", "136 (STRAWBERRY LUNCH BOX WITH CUTLERY) \n", "137 (LUNCH BAG APPLE DESIGN) \n", "138 (LUNCH BAG APPLE DESIGN) \n", "139 (PLASTERS IN TIN CIRCUS PARADE , POSTAGE) \n", "140 (LUNCH BAG RED RETROSPOT, POSTAGE) \n", "\n", " consequents antecedent support \\\n", "0 (POSTAGE) 0.0651 \n", "1 (SET/6 RED SPOTTY PAPER CUPS) 0.0868 \n", "2 (SET/6 RED SPOTTY PAPER PLATES) 0.0868 \n", "3 (SET/6 RED SPOTTY PAPER CUPS) 0.0716 \n", "4 (SET/6 RED SPOTTY PAPER PLATES) 0.0716 \n", ".. ... ... \n", "136 (LUNCH BOX WITH CUTLERY RETROSPOT ) 0.1041 \n", "137 (LUNCH BAG SPACEBOY DESIGN ) 0.1085 \n", "138 (LUNCH BAG RED RETROSPOT) 0.1085 \n", "139 (PLASTERS IN TIN SPACEBOY) 0.1258 \n", "140 (LUNCH BAG APPLE DESIGN) 0.1041 \n", "\n", " consequent support support confidence lift leverage conviction \n", "0 0.6746 0.0651 1.0000 1.4823 0.0212 inf \n", "1 0.1171 0.0846 0.9750 8.3236 0.0744 35.3145 \n", "2 0.1085 0.0846 0.9750 8.9895 0.0752 35.6616 \n", "3 0.1171 0.0694 0.9697 8.2783 0.0610 29.1345 \n", "4 0.1085 0.0694 0.9697 8.9406 0.0617 29.4208 \n", ".. ... ... ... ... ... ... \n", "136 0.1236 0.0542 0.5208 4.2124 0.0414 1.8289 \n", "137 0.1041 0.0564 0.5200 4.9942 0.0451 1.8664 \n", "138 0.1323 0.0564 0.5200 3.9298 0.0420 1.8077 \n", "139 0.1193 0.0629 0.5000 4.1909 0.0479 1.7614 \n", "140 0.1085 0.0521 0.5000 4.6100 0.0408 1.7831 \n", "\n", "[141 rows x 9 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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