📊 ABC Company Employee Data Analysis Project

A comprehensive Data Analytics & Visualization Project focused on extracting meaningful insights from employee data using Python.

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🚀 Run Notebook in Google Colab

Click below to open the notebook:

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📘 Project Overview

This project provides a detailed analysis of employee data from ABC Company.

• Dataset contains 458 rows and 9 columns

• Focuses on workforce insights, salary distribution, and team structure

• Builds a data-driven report for business decision-making

🎯 Objective

The project includes:

🔹 Data Preprocessing

🔹 Exploratory Data Analysis (EDA)

🔹 Business-driven analytical tasks

🔹 Data visualizations

🔹 Insight generation & storytelling

🔹 Clean and reproducible Google Colab notebook

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📂 Dataset Description

Column Name	Description
empid	Employee ID
age	Age of the employee
gender	Gender
height	Height (corrected during preprocessing)
weight	Weight
team	Department
position	Job role
salary	Monthly salary
experience	Years of experience

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🧹 Data Preprocessing

The following preprocessing steps were performed:

✔ Ensured dataset consistency and quality

✔ Standardized column names (lowercase, underscores)

✔ Handled missing values

• Numeric → Median
• Categorical → Mode / "Unknown"

✔ Removed duplicate records

✔ Corrected height values (150–180 cm range)

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📈 Analysis Tasks

🔍 Task 1: Team Distribution

• Employee count by team

• Percentage distribution

• Identified largest team

• Visualized using bar & pie charts

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🧑‍💼 Task 2: Employee Position Segregation

• Grouped employees by job role

• Percentage distribution

• Identified common roles

• Visualized using count plot

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🎂 Task 3: Predominant Age Group

• Created age groups (bins)

• Counted distribution

• Visualized using bar chart

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💰 Task 4: Salary Expenditure Analysis

• Total salary by team

• Total salary by position

• Visualized using bar charts

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📉 Task 5: Age–Salary Relationship

• Calculated correlation between age and salary

• Visualized using scatter plot

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🎨 Visualizations

Analysis	Visualization
Team Distribution	Bar chart, Pie chart
Position Segregation	Count plot
Age Groups	Bar chart
Salary Analysis	Bar charts
Age vs Salary	Scatter plot

✔ Consistent theme, labels, and color palette applied across all charts

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🧠 Key Insights

✔ Majority of employees fall in the 25–35 age group

✔ A positive correlation exists between age and salary

✔ Senior roles contribute significantly to salary expenditure

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📌 Business Impact

This analysis helps organizations to:

• Plan workforce distribution

• Optimize salary budgets

• Improve hiring strategies

• Support data-driven decision-making

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⚠️ Limitations

• The dataset is relatively small (458 records), which may limit the depth of insights.

• Data is static and does not reflect real-time workforce changes.

• Some values (e.g., height) were artificially adjusted, which may affect analysis accuracy.

• Limited features; important factors like performance ratings, department budgets, or education level are not included.

• Correlation analysis does not imply causation between variables (e.g., age and salary).

• Results may not generalize to other organizations with different workforce structures.

• Visualization-based insights are descriptive and do not include predictive modeling.

🛠 Tech Stack

Tool	Purpose
Python	Programming language
Pandas	Data processing
NumPy	Numerical operations
Matplotlib / Seaborn	Visualization
Google Colab	Development environment

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📁 Repository Structure

ABC-Company-Employee-Analysis/

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├── ABC Company.xlsx

├── Python Module End Assessment 2.ipynb

├── README.md

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🚀 How to Run the Project

1️⃣ Open Notebook

Click the Google Colab link above

2️⃣ Run Cells

Execute all cells sequentially

3️⃣ View Results

Explore visualizations and insights

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📌 Academic Submission

This repository was created as part of a Python Module End Assessment in a Data Analytics program to demonstrate data preprocessing, exploratory data analysis (EDA), visualization techniques, and deriving business insights from employee data.

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👤 Author

Name: Laya Mary Joy

Organization: Entri Elevate

Date: January 15, 2026

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⭐ Acknowledgment

Thanks to Entri Elevate for guidance and support.

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📌 Future Improvements

• Add interactive dashboards (Power BI / Streamlit)

• Include advanced statistical analysis

• Integrate real-time employee datasets

• Enhance visualization interactivity

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