Can the Plane Fly? - README

Overview

This Shiny application predicts whether a plane can fly based on weather conditions. The prediction is made using the SVM model trained on a dataset of historical weather data and flight outcomes.

Prerequisites

Before running the application, ensure you have the following R packages installed:

• shiny
• e1071
• shinythemes

To install these packages, run:

install.packages(c("shiny", "e1071", "shinythemes"))

Dataset

The dataset used in this application includes the following features:

• outlook: Weather outlook (Sunny, Overcast, Rainy)
• temperature: Temperature in Fahrenheit
• humidity: Humidity percentage
• windy: Whether it is windy (TRUE or FALSE)
• fly: Whether the plane can fly (yes or no)

The dataset is hardcoded within the app and consists of 14 samples.

How to Run the Application

1. Open RStudio or any R environment.
2. Ensure all necessary packages are installed.
3. Copy the entire code into an R script file (e.g., app.R).
4. Run the script. The Shiny application will launch in your default web browser.

Application Interface

Input Parameters

• Outlook: Select the weather outlook (Sunny, Overcast, Rainy).
• Temperature: Use the slider to select the temperature (64°F to 86°F).
• Humidity: Use the slider to select the humidity (65% to 96%).
• Windy: Select whether it is windy (Yes or No).
• Submit Button: Click to submit the input values and get the prediction.

Output

• Status/Output Text Box: Displays the status of the calculation.
• Prediction Results Table: Displays the prediction result (yes or no) indicating whether the plane can fly under the given conditions.

Model

The application builds the SVM model using the e1071 package to predict the flight status. The model is trained on the provided dataset and used to make predictions based on user input.

Code Structure

• UI: Defined in the ui variable using fluidPage. It includes input fields, a submit button, and output areas.
• Server: Defined in the server function. It handles the logic for reading inputs, building the model, making predictions, and displaying the results.
• Shiny App: The shinyApp function combines the UI and server components to create the Shiny application.

Debugging

For debugging purposes, the input data is printed to the console before making predictions. This can help ensure that the input values are correctly captured and processed.

Example Usage

1. Select "Sunny" for outlook.
2. Set the temperature to 70°F.
3. Set the humidity to 90%.
4. Select "Yes" for windy.
5. Click "Submit".
6. The prediction result will be displayed in the "Prediction results table".

Conclusion

This Shiny application provides an interactive interface to predict whether a plane can fly based on weather conditions using a machine learning model. It demonstrates the use of Shiny for building web applications in R and the SVM application for classification tasks.

CONTACTS

You may reach me through: Email: kassimchambulilo@gmail.com phone: +255 613 048 665