This repository contains code and datasets for classifying mobile prices based on their specifications. The dataset used in this project is sourced from the Kaggle website and contains information on various mobile specifications such as battery power, camera quality, etc.
The code is written in R programming language and uses various libraries such as tidyverse, caret, nnet, caTools, etc. The following algorithms have been implemented for classification:
SVM Random Forest Decision Tree Apart from these, some additional algorithms have also been implemented, such as correlation-based feature selection and varimp (variable importance) algorithm.
The train.csv dataset has been used for training the algorithms and the test.csv dataset has been used for testing the performance of the algorithms.
Various graphs and plots have been created to visualize the relationships between the different features in the dataset. These include scatter plots, box plots, histograms, etc.
The results of each algorithm have been saved in the results/ directory. Each algorithm has a separate file with the suffix _results.txt and contains information on the accuracy, confusion matrix, and ROC curve of the algorithm.
The data visualization plots have been saved in the results/data_visualization_plots/ directory.
This project is licensed under the MIT License - see the LICENSE file for details.