2024-Salary-Analysis-for-Machine-Learning-Engineers

Project Overview

This project provides an interactive Shiny Dashboard for exploring salary distributions of professionals working in Machine Learning and Data Science fields.
It allows filtering by experience level, job title, company size, and country to better understand salary patterns in 2024.

The dashboard offers a user-friendly and dynamic interface built with shinydashboard, enabling deep data exploration and interactive visualizations.

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Features

• Dynamic Filtering

  • Filter salaries by:
    • Experience Level (EN, MI, SE, EX)
    • Job Title
    • Company Size (S, M, L)
    • Employee Residence Country

• Interactive Visualizations

  • Plotly-powered box plots showing salary distribution by experience level
  • Smooth, modern UI/UX design with shinydashboard

• Automatic Data Cleaning

  • Missing values are automatically removed (na.omit())

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Tech Stack

Category	Libraries
Web App Framework	shiny, shinydashboard
Data Manipulation	tidyverse, dplyr
Visualization	ggplot2, plotly

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Dataset: salaries.csv

This dataset contains salary information for data-related roles across multiple countries and company sizes.

Column	Description
work_year	Year of the salary report
experience_level	Experience level (EN, MI, SE, EX)
employment_type	Type of employment (FT, PT, CT, FL)
job_title	Job title
salary	Salary in local currency
salary_currency	Currency of the salary
salary_in_usd	Salary converted to USD
employee_residence	Country of residence
remote_ratio	Percentage of remote work (0–100)
company_location	Country of the company
company_size	Size of the company (S, M, L)

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Previous EDA & ML Analysis

Before building the dashboard, an Exploratory Data Analysis (EDA) was performed:

• Summary statistics and data cleaning
• Average salary grouped by experience, country, and company size
• Visualization of salary vs remote ratio and country-based salary trends
• Correlation heatmap to explore variable relationships

Machine Learning Models:

• Linear Regression model for salary prediction
• Random Forest model to identify key factors affecting salaries

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How to Run the App

# Install dependencies
install.packages(c("shiny", "shinydashboard", "tidyverse", "dplyr", "ggplot2", "plotly"))

# Run the app
library(shiny)
runApp("app.R")