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Weka project 333

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Sirapop Kongpatyuen

This document discusses three machine learning techniques applied to a dataset: 1. Clustering was performed and identified 11 clusters as the "knee of the curve". 2. Classification was used to predict whether a customer wears glasses based on their 7-11 purchases. It was able to predict correctly for 9 out of 10 customers, achieving a 90% accuracy rate. 3. Association analysis was also applied but no details were provided.

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CP463 Artificial Intelligence WEKA PROJECT Sirapop Kongpatyuen 55102010333
Data
Data(Con)
Data(Con)
Clustering Knee of the curve is 11
Clustering(Con) 1Facewash Bread Tissue Hamburger YenYen DORM THIN NO Glass Single NO Smoke NO Alcoholic 2Pen DORM THIN NO Glass Single NO Smoke NO Alcoholic 3Candy Cookie DrinkingWater Slurpee Icecream Frozen-food DORM THIN Glass Single NO Smoke NO Alcoholic 4Facewash Prepaid Bread Tissue Ice Liquor Cigarett HOME FAT NO Glass InRelationship Smoke Alcoholic 5Pen Sausage Icecream Frozen-food DORM THIN NO Glass Single NO Smoke NO Alcoholic 6Icecream Bean Jelly Yoghurt Liquor Cigarrett HOME FAT Glass InRelationship Smoke Alcoholic 7Pen Sandwich Frozen-food DORM THIN NO Glass Single NO Smoke NO Alcoholic 8Candy Pen Cookie Sausage DrinkingWater Slurpee DORM THIN Glass Single NO Smoke NO Alcoholic 9Icecream Frozen_Food Bean Liquor HOME FAT Glass InRelationship NO Smoke Alcoholic 10Candy Cookie DrinkingWater DORM THIN NO Glass Single NO Smoke NO Alcoholic 11Facewash Prepaidcard Bread Softdrink Hamburger Liquor HOME FAT NO Glass InRelationship NO Smoke Alcoholic
Classification ??????????????????? 7-11 ??????????????????????????????????????????? Data
Classification(Con) ???????????
Classification(Con) ??????????????????????? 1 ??? 10 ?????? 90%
Association

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Weka project 333