This project aims to analyze and discover the common standard features shared by various tech gadgets from various brands that help the brand stand out in the market.
Different brands of tech gadgets with various features are chosen for analysis and collected via typeform - 129(Respondents)
Gadgets names – Smartphone, Mouse Keyboard, Headphone Camera and Laptop
- The collected datas are then separated based on different gadgets namely Camera , Headphones , Keyboard , Mouse and Smart Phone
- The 5 point scale gadgets specfic Feature questions are alone extracted and kept in a csv file
Each of the technological gadgets is available in a variety of brands. In this modern era, it is difficult to determine which brand a customer should purchase based on their preferences, so clustering analysis and PCA analysis are used to classify the brand based on the characteristics and standard features shared by each brand.
Smartphone brand data is analyzed and clustered based on feature similarities, and the most important features that contribute the most to the brand are identified using dimensionality reduction using PCA.
- Clustering Analysis using K- Means Algorithm - - data classification based on characteristic similarity
- Principle Component Analysis - to identify the variables that have the greatest impact on all brands
- Data Collection - Typeform
- Data Analysis - R- Studio
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The variables Performance, Quality, and Design contribute the most to the PC1 component, while Price and Performance contribute the most to the PC2 component. This means that the greater the contribution value, the more the variable contributes to the component.
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the variables
Get. Used to. Habit, Brand
contributes more to the cluster 1Services, Reputation, Value, and Quality
contribute more to the cluster 2Operating Platform/System, Design, Performance, and Price
contribute more to the cluster 3
When comparing the clusters with the brands, the above characteristic best fits the top three brands, namely Samsung, Xiaomi, and Apple
and we can say that customers buys smartphones from these brands based on the characteristic depicted by each of the three clusters regarding the variables.
For example :
- The person who seeks for best design, price, and performance can buy Apple Smartphones
- The person who is Brand-specific will buy Samsung smartphones
- The person who strives for the best Quality goes for Xiaomi smartphones
The above clustering analysis creates clusters based on customers' preferences for brands, and through PCA analysis, the features that contribute the most to each brand of smartphone are identified and validated with the clusters, making it easier for consumers to select brands based on their preferences. For each brand, the output is verified and compared with survey respondents. The techniques described above can also be applied to other types of technology to determine brand categorization based on features. • Smartphone - Samsung • Mouse - Logitech • Headphone - Oneplus • Camera – Canon • Keyboard - Apple • Laptop - HP