CoreHR - Human Resources Department Project

📌 Overview

This project focuses on analyzing employee data to gain insights into various human resources metrics. The goal is to understand factors affecting employee performance, satisfaction, and retention.

📂 Project Structure

├── Human_Resources_project_png/      # Visualizations generated during analysis
├── Human_Resources.csv               # Dataset containing employee information
├── Human_Resources_Department_Project.ipynb  # Jupyter Notebook with analysis and findings
├── README.md                         # Project documentation

📊 Dataset

The dataset, Human_Resources.csv, includes employee information with features such as:

• Employee ID: Unique identifier for each employee.
• Age: Employee's age.
• Department: Department in which the employee works.
• Education: Level of education attained.
• Job Role: Specific role or position of the employee.
• Marital Status: Marital status of the employee.
• Years at Company: Tenure of the employee within the company.
• Job Satisfaction: Self-reported job satisfaction level.
• Performance Rating: Performance evaluation score.

🚀 Installation

1️⃣ Clone the repository:

git clone https://github.com/27abhishek27/Human-Resources-Department-Project.git
cd Human-Resources-Department-Project

2️⃣ Install dependencies:

Ensure you have the following Python packages installed:

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

You can install them using pip:

pip install pandas numpy matplotlib seaborn scikit-learn

🔍 Methodology

1. Data Preprocessing

• Handling Missing Values: Identified and addressed any missing data in the dataset.
• Encoding Categorical Variables: Converted categorical features such as Department, Education, and Job Role into numerical representations.
• Feature Scaling: Standardized numerical features to ensure uniformity.

2. Exploratory Data Analysis (EDA)

• Visualized distributions of key features to understand data patterns.
• Correlation Analysis: Examined relationships between different features to identify potential predictors of employee satisfaction and performance.
• Trend Analysis: Investigated trends across departments, job roles, and other categorical variables.

3. Model Building

• Predictive Modeling: Developed machine learning models to predict employee performance ratings based on available features.
• Model Evaluation: Assessed model performance using metrics like accuracy, precision, recall, and F1-score.

📊 Visualizations

Here are some visualizations from the project:

🛠️ Technologies Used

• Python
• Pandas & NumPy
• Matplotlib & Seaborn
• Scikit-learn
• Jupyter Notebook

📌 Future Improvements

• Advanced Predictive Models: Implement more complex models to improve prediction accuracy.
• Employee Retention Analysis: Extend the analysis to understand factors influencing employee retention.
• Interactive Dashboards: Develop dashboards to allow dynamic exploration of HR metrics.