This repository contains a dataset and R code for mobile classification. The dataset includes information on various mobile phones, such as their features, specifications, and prices. The R code implements a machine learning model to classify mobile phones into different categories based on their features.
The dataset, named mobile_data.csv, is stored in the data directory. It is a comma-separated values (CSV) file that contains information on mobile phones, with each row representing a different mobile phone and each column representing a different attribute or feature of the mobile phones. The dataset includes the following columns:
Brand: The brand or manufacturer of the mobile phone Model: The model name or number of the mobile phone OS: The operating system used in the mobile phone (e.g., Android, iOS) RAM: The amount of RAM (Random Access Memory) in the mobile phone in GB Storage: The storage capacity of the mobile phone in GB Camera: The camera specifications of the mobile phone in megapixels Price: The price of the mobile phone in USD Class: The target class label indicating the category of the mobile phone (e.g., budget, mid-range, premium) The dataset has been preprocessed and cleaned for analysis, and it is ready to be used for mobile classification.
The R code for mobile classification is stored in the code directory. It includes the following files:
mobile_classification.R: This is the main R script that implements the machine learning model for mobile classification. It reads the mobile_data.csv dataset, performs data preprocessing, trains the machine learning model using a classification algorithm, and evaluates the model's performance. The script also includes visualization of the results and provides insights into the classification process.
helper_functions.R: This file contains helper functions used in the mobile_classification.R script, such as functions for data preprocessing, feature engineering, and model evaluation.
The R code in this repository requires the following R packages:
tidyverse: For data manipulation, visualization, and analysis caret: For machine learning model training and evaluation You can install these packages using the following R command:
r Copy code install.packages(c("tidyverse", "caret")) Usage To use the R code for mobile classification, follow these steps:
Clone or download this repository to your local machine. Install the required R packages as mentioned in the "Dependencies" section. Open the mobile_classification.R script in R or RStudio. Run the script to load the dataset, preprocess the data, train the machine learning model, and evaluate its performance. Review the results and interpretation provided in the script for insights into the classification process. Modify the code as needed for your specific use case or experiment with different machine learning algorithms or parameter settings. Feel free to use, modify, or extend the code for your own projects or research purposes. Contribution If you would like to contribute to this repository, you can fork the repository, make changes or improvements, and submit a pull request. Contributions, bug reports, and feedback are welcome and appreciated!
This repository is released under the MIT License, which allows for free use, modification, and distribution of the code and dataset, with attribution to the original authors.
If you have any questions or comments, please feel free to contact the authors:
Ripul Handoo