https://github.com/hibashajahan55/python-abc-company-analysis
https://github.com/hibashajahan55/python-abc-company-analysis
jupyter-notebook matplotlib numpy pandas python seaborn
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/hibashajahan55/python-abc-company-analysis
- Owner: Hibashajahan55
- Created: 2025-02-08T16:30:01.000Z (13 days ago)
- Default Branch: main
- Last Pushed: 2025-02-09T17:57:42.000Z (12 days ago)
- Last Synced: 2025-02-09T18:34:03.460Z (12 days ago)
- Topics: jupyter-notebook, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# PYTHON-ABC-COMPANY-ANALYSIS
# INTRODUCTION
This project involves analyzing salary expenditures of ABC Company to identify trends and disparities across teams and positions. The dataset contains 458 rows and 9 columns, and the goal is to process, analyze, and visualize employee data. The findings will help in understanding workforce distribution, salary allocation, and possible correlations.
# THEORY
Data analysis plays a crucial role in human resource management, especially in understanding salary trends, age distributions, and team-based allocations. By analyzing this dataset, we can:
* Identify which teams and positions have the highest salary expenditures.
* Explore the correlation between age and salary.
* Understand employee distribution across teams and positions.
* Use visualizations like bar charts, scatter plots, and heatmaps to enhance interpretability.
# PROJECT STEPS
### 1. Preprocessing
* Load the dataset and inspect for missing values or inconsistencies.
* Replace incorrect values in the "height" column with random values between 150 cm and 180 cm.
* Ensure data integrity and consistency before analysis.
### 2. Analysis Tasks
* Employee Distribution Across Teams: Count employees per team and calculate percentage distributions.
* Segregation Based on Position: Identify the most common employee positions.
* Predominant Age Group: Determine which age group has the most employees.
* Highest Salary Expenditure: Identify which team and position have the highest salary allocation.
* Correlation Between Age and Salary: Explore whether salary increases with age.
### 3. Graphical Representations
The graphs included in the project are:
1. Employee Distribution Across Teams (Bar Chart)
2. Employees by Position (Pie Chart)
3. Predominant Age Group (Histogram)
4. Salary Expenditure (Bar Charts)
5. Age vs Salary (Scatter Plot)
6. Salary Expenditure by Team and Position (Heat map)
# TECHNOLOGIES USED
#### Python: Primary programming language.
#### Pandas: Data manipulation and analysis.
#### NumPy: Numerical computations and data transformations.
#### Seaborn & Matplotlib: Data visualization.
#### Jupyter Notebook: Development environment.
# CONCLUSION
This project provided a comprehensive analysis of ABC Company’s employee salary distribution. The results help in understanding salary allocation across teams and positions, as well as identifying key trends in employee demographics. The insights can assist in better financial planning and talent management strategies within the organization.