📊 Employee Burnout Prediction using Linear Regression

🔍 Overview

This project predicts employee burnout rate using a Linear Regression model.

It uses important workplace factors like:

• Resource Allocation
• Mental Fatigue Score
• Work From Home Setup

---

📁 Dataset

🧾 Sample Data Preview

---

📊 Dataset Shape

• Total Rows: 22,750
• Columns: 9

---

❗ Missing Values Analysis

---

📈 Statistical Summary

---

🔢 Unique Values

---

⚙️ Data Preprocessing

🧹 Cleaning & Encoding

Steps:

• Dropped unnecessary columns (Employee ID, Date of Joining)
• Removed missing values
• Applied One-Hot Encoding

---

🤖 Model Building

🏗️ Train-Test Split

🧠 Model Training

• Model Used: Linear Regression

---

📊 Model Evaluation

📉 Performance Metrics

• Mean Squared Error (MSE): ~0.003
• R² Score: ~0.92 ✅

---

🛠️ Technologies Used

• Platform: Google Colab
• Language: Python

Libraries:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

---

▶️ How to Run

1. Clone the repository:

git clone https://github.com/Caktusuki/Internship.git

2. Open in Google Colab

3. Install dependencies:

pip install pandas scikit-learn matplotlib seaborn openpyxl

4. Run all cells

---

📬 Contact

• Name: Vikyraj Deka
• Email: vikyrajdekawork@gmail.com

---

⭐ If you like this project, give it a star!

---

## 🔥 What makes this good

- Clean structure  
- Proper image linking (using `Preview/`)  
- Easy to understand  
- Looks professional on GitHub  

---

If you want next upgrade:
👉 I can add **badges, project demo style, and make it resume-level strong** 🚀