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Predictive Modeling

Overview

This project involves developing a predictive model using R for analyzing and forecasting based on historical data. The primary focus is to create an efficient model that can provide meaningful insights and predictions by leveraging machine learning techniques and statistical analysis. The project uses real-world datasets to illustrate different approaches to predictive modeling, including data preprocessing, model building, and evaluation.

Features

  • Data preprocessing and cleaning
  • Exploratory data analysis (EDA)
  • Model selection and training
  • Evaluation of model performance using various metrics
  • Visualization of results

Requirements

  • R version 4.0 or higher
  • RStudio (recommended for easier development)
  • Required R packages:
    • caret
    • randomForest
    • ggplot2
    • dplyr

Install the required packages using:

install.packages(c("caret", "randomForest", "ggplot2", "dplyr"))

Usage

  1. Clone this repository to your local machine:
git clone https://github.com/urstrulyrithik/Predictive-Modeling

  1. Open the R script (predictiveModeling.R) in RStudio or your preferred R environment.

  2. Run the script to execute the data preprocessing, model building, and evaluation steps.

Project Structure

  • predictiveModeling.R: Main R script containing the code for data analysis and predictive modeling.
  • data/: Directory where datasets are stored.
  • plots/: Contains graphs generated during the exploratory data analysis.
  • Project Document: Contains the PDF documentation of the project.
  • README.md: Project documentation.

Results

The model's performance metrics, such as accuracy, precision, recall, and F1-score, are displayed at the end of the script. Graphs generated during the exploratory data analysis (EDA) and model evaluation are saved in the plots/ directory for further insights.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributions

Contributions are welcome! If you'd like to improve the code or add new features, please fork the repository and create a pull request.

Contact

For any questions, feel free to reach out at: