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📊 Data Analysis & Decision Tree Modeling using R

R Data Science Status

📌 Project Overview

This project demonstrates data preprocessing, exploratory data analysis (EDA), and decision tree modeling using R.

The goal is to clean missing data, explore patterns, and build predictive models to support data-driven decision making.

🎯 Objectives

  • Handle missing values in datasets
  • Perform Exploratory Data Analysis (EDA)
  • Build Decision Tree models
  • Generate insights using R programming

🧠 Workflow

  1. Data Cleaning & Missing Value Handling
  2. Exploratory Data Analysis
  3. Feature Exploration
  4. Decision Tree Modeling

📂 Project Structure

R-Project
│
├── DSmiss.R          # Missing data handling
├── EDA.R             # Exploratory Data Analysis
├── decision_tree.R   # Decision Tree Model
├── FINAL PPT.pptx    # Project Presentation
└── README.md

🛠️ Tools & Libraries

  • R Programming
  • dplyr
  • ggplot2
  • rpart
  • caret

🚀 How to Run

Open scripts in RStudio and execute:

source("EDA.R")
source("decision_tree.R")
source("DSmiss.R")

📈 Skills Demonstrated

✔ Data Cleaning in R
✔ Statistical Analysis
✔ Decision Tree Modeling
✔ Data Visualization

👨‍💻 Author

Aditya Charan Eranki
Data Science | Machine Learning | Analytics