mobilepriceclassification

There are 0 repository under mobilepriceclassification topic.

/ Mobile-Price-Range-Prediction-A-Data-Science-Approach
Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.
data-science knn-classification logistic-regression machine-learning mobilepriceclassification rbf-kernel svm-classifier python
Language:Jupyter Notebook 3
/ Mobile-Price-Classification
Mobile price classification using SVM with hyperparameter tuning and feature selection technique. Accuracy is 95%.
mobilepriceclassification priceclassification eda feature-selection hyperparameter-tuning mutual-information svm-classifier svm-model cvscoring selectkbes svmhyperperameter
Language:Jupyter Notebook

/ Mobile-Price-Range-Prediction-A-Data-Science-Approach