https://github.com/dipeshgoyal013/salary-data-analysis

Salary Analysis according department and agency.
https://github.com/dipeshgoyal013/salary-data-analysis

analysis matplotlib numpy pandas salary sklearn-library

Last synced: 2 months ago
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Salary Analysis according department and agency.

• Host: GitHub
• URL: https://github.com/dipeshgoyal013/salary-data-analysis
• Owner: dipeshgoyal013
• Created: 2024-08-03T10:56:16.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-03T11:23:27.000Z (almost 2 years ago)
• Last Synced: 2025-10-25T02:44:16.111Z (8 months ago)
• Topics: analysis, matplotlib, numpy, pandas, salary, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 765 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Salary-Data-Analysis

### Tools Used

Python Version: 3.7

Packages: pandas, numpy, sklearn, matplotlib, seaborn

### Featuring Engineering

Creating New Column using Date Column for Better analysis

![image](https://github.com/user-attachments/assets/e2c9a198-1a46-4f2b-8370-ecc1c7f326b6)

### EDA

I looked at the distributions of the data and the value counts for the various categorical variables. Below are a few highlights from the tables.

![image](https://github.com/user-attachments/assets/3a7e3003-b21b-4f47-bf90-ef04aa610659)

![image](https://github.com/user-attachments/assets/b1167697-fc25-4c4f-b391-8fc9906405f5)

### Model Building

First, I transformed the categorical variables into dummy variables using encoding technique. I also split the data into train and tests sets with a test size of 25%.

I tried Linear Regression models and evaluated them using Mean Squared Error.