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Mobile price classification

Abstract:
Algorithms for machine learning (ML) are utilised in a wide range of industries, including e-commerce, banking, education, and industry. The performance of ML algorithms varies based on the dataset and processing stages. To get decent results, selecting the appropriate algorithm, preprocessing, and postprocessing techniques is crucial. In order to forecast the pricing class of mobile phones, this study compares the Random Forest Classifier, Logistic Regression Classifier, Decision Tree Classifier, Linear Discriminant Analysis, K-Nearest Neighbor Classifier, and SVC techniques. Methods are assessed using data from Kaggle.com's "Mobile Price Classification" dataset. First, the dataset's values are all examined to make sure there are no missing entries. The dataset is then scaled in order to produce more useful data for ML algorithms. Then, to obtain relevant features, feature selection techniques that lower the computational cost by minimising the number of inputs are used. Finally, classification algorithm parameters are adjusted to boost system accuracy. The ANOVA f-test feature selection method is more practical for this dataset, as shown by the findings. It provides adequate accuracy with the fewest possible features. In comparison to other models, it can also be seen that the SVC classifier has the highest test accuracy.

final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment 3.classes will held accordingly.

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