Objective: The project focuses on categorizing and classifying smartphone processors based on their performance metrics, architecture, and features. This classification aids consumers in making informed decisions when purchasing smartphones and helps manufacturers benchmark their processors against competitors.
Methodology: The project involves collecting data on various smartphone processors, including parameters such as clock speed, number of cores, manufacturing technology, and benchmark scores. Machine learning algorithms such as Decision Trees, Random Forests, and Support Vector Machines (SVM) are applied to classify the processors into different performance tiers. The model's accuracy and reliability are validated using cross-validation techniques, and the results are presented
through an interactive dashboard for easy comparison and analysis.
@SyedImtiyaz-1, assign me this issue under GSSOC'24
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Feature Description
Objective: The project focuses on categorizing and classifying smartphone processors based on their performance metrics, architecture, and features. This classification aids consumers in making informed decisions when purchasing smartphones and helps manufacturers benchmark their processors against competitors.
Methodology: The project involves collecting data on various smartphone processors, including parameters such as clock speed, number of cores, manufacturing technology, and benchmark scores. Machine learning algorithms such as Decision Trees, Random Forests, and Support Vector Machines (SVM) are applied to classify the processors into different performance tiers. The model's accuracy and reliability are validated using cross-validation techniques, and the results are presented through an interactive dashboard for easy comparison and analysis.
@SyedImtiyaz-1, assign me this issue under GSSOC'24
Use Case
NA
Benefits
No response
Add ScreenShots
No response
Priority
High
Record