Elements of Branding Product Differentiation Relevance Perceived Value.

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Presentation transcript:

Elements of Branding Product Differentiation Relevance Perceived Value

Data cleaning Data integration Data selection Data transformation Data mining Pattern evaluation Knowledge presentation

Client Data source in Karachi Clean Transform Integrate Load Data warehouse Query and Analysis tools Data source in Lahore Data source in Islamabad Client Data source in Faisalabad

Evaluation and Presentation Knowledge Data Mining Patterns Selection and Transformation Data Warehouse Cleaning and Integration Databases

Types of Data Mining Classification Association Characterization Clustering

Education : Matric or Below Marital Status : unmarried Training Data Test Data M 55% 56% F 45% 44% Age >=20<=25 years Location : rural area M 60% 59% F 40% 41% M 40% 39% F 60% 61% Education : Matric or Below Marital Status : unmarried M 62% 64% F 38% 36% M 35% 36% F 65% 64% Annual Income < one lac M 65% 66% F 35% 34%

Efficiency = actual/theoretical*100 = 50/66*100= 75.75 %

Confidence = Transactions (eggs+milk) Transactions (eggs or milk or both) e.g, 25/75*100=33.3% Support = Transactions (eggs+milk) Total no. of transactions e.g, 10/50*100 = 20%