This project explores salary trends using Exploratory Data Analysis (EDA). The goal is to uncover insights into salary distributions, job roles, industries, and company factors that influence compensation. Through 19 high-quality visualizations, we analyze the relationship between job roles, employment status, company ratings, and salary trends.
The dataset contains structured salary information across various job roles, employment types, industries, and locations. The analysis focuses on:
To successfully run the project and generate the visualizations, ensure you have the following Python libraries installed:
pip install pandas matplotlib seaborn folium wordcloud numpy
This visualization represents the overall salary distribution in the dataset, highlighting salary concentration and outliers.
A Kernel Density Estimate (KDE) plot illustrating the probability density of different salary ranges.
Compares salaries based on employment type (Full-time, Part-time, Contract).
Visualizes the variation in salaries across different job titles.
Displays the most frequently occurring job titles in the dataset.
A bar chart showcasing the most common job roles in the dataset.
Identifies the locations offering the highest average salaries.
Highlights the companies offering the best compensation.
Illustrates how different job roles and employment statuses impact salary ranges.
A trend analysis of how salaries fluctuate across various job positions over time.
Examines how different industries pay over time.
Shows how salary varies across different employment types.
Explores the variance in salaries when considering both job roles and employment status.
Analyzes whether higher-rated companies offer better salaries.
Detects anomalies in salary distribution, identifying potential data issues or extreme values.
Visualizes how salary trends shift based on job role and employment type.
A correlation matrix heatmap showcasing relationships between salary and other features.
Examines the salary spread for different job roles.
Shows the highest-paying job roles in the dataset.
Explore the interactive **Salary Insights Dashboard** created using **Tableau** to gain deeper insights into salary distributions, job roles, industries, and company factors influencing compensation.
Author: Dhruv Trivedi