Statistical-Computing-Empirical-Methods-Using-R-Programming/setup.txt at main · thrisharajkumar/Statistical-Computing-Empirical-Methods-Using-R-Programming · GitHub

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==============================
Statistical Computing & Empirical Methods – Setup Guide
==============================

This project uses R programming for statistical simulation, EDA, and supervised learning.
Please follow the instructions below to set up your environment and run the code.

----------------------------------------
🔧 Requirements
----------------------------------------

Make sure you have the following installed:

1. R (version >= 4.1.0)
   https://cran.r-project.org/

2. RStudio (optional but recommended)
   https://posit.co/download/rstudio-desktop/

----------------------------------------
📦 Required R Packages
----------------------------------------

Install the following packages in R before running any scripts:

```r
install.packages(c(
  "tidyverse",      # data wrangling and plotting
  "rpart",          # decision trees
  "randomForest",   # random forest models
  "caret",          # model evaluation and training
  "ggplot2",        # plotting
  "reshape2",       # data reshaping
  "boot",           # bootstrapping and simulation
  "e1071",          # miscellaneous statistical functions
  "knitr"           # for rendering R Markdown
))
```

----------------------------------------
📁 Directory Overview
----------------------------------------

- scripts/     → Contains R scripts for modeling and simulation
- notebooks/   → R Markdown files with reproducible reports
- reports/     → Final PDF submissions (Section A, B, C)
- images/      → Generated visualizations for README and markdown
- README.md    → Overview of the project
- setup.txt    → This setup guide

----------------------------------------
📊 Data Files (Note)
----------------------------------------

This repository does **not** include the raw Kaggle dataset due to licensing.

Download it manually from:
https://www.kaggle.com/datasets/krishnaraj30/finance-loan-approval-prediction-data

Save `train.csv` and `test.csv` in the root of your project or wherever needed.

----------------------------------------
▶️ How to Run
----------------------------------------

Open RStudio or your terminal, then:

1. Run scripts directly:
   - `decision_tree_model.R`
   - `random_forest_model.R`
   - `bootstrap_simulation.R`
   - `clt_visualisation.R`

2. Or render markdown reports:
   ```r
   rmarkdown::render("notebooks/SectionA_Code_Output.Rmd")
   ```


**Thrisha Rajkumar** – University of Bristol

This project is part of the Statistical Computing & Empirical Methods unit.