• Tayaba Anwar
• Hamna Qaseem
• Amina Saeed

In today's world, salary is one of the most important factors for a person's professional growth and success. In this context, it is important to have a good understanding of how salaries are determined and what factors affect them. The purpose of this project is to build a salary prediction web application that can help individuals understand the factors that influence their salary and how they can use this information to make informed decisions about their career path.

The main objective of this project is to build a web application that can predict the salary of an individual based on various factors such as education, experience, location, job role, and company size. This application will help individuals understand how their salary compares to industry standards and what steps they can take to improve their earnings.

The methodology used in this project involves collecting data on various factors that influence salaries and building a machine learning model that can predict salaries based on this data. The data is collected from various sources such as job portals, salary surveys, and company websites. The data is then cleaned, processed, and analyzed to identify patterns and trends.

The machine learning model used in this project is a regression model that can predict salaries based on various input variables. The model is trained on a dataset consisting of historical salary data and various input variables. The performance of the model is evaluated using various metrics such as mean absolute error, mean squared error, and R-squared score.

The web application is built using the Streamlit framework, which is a powerful and easy-to-use tool for building data-driven web applications. The application consists of a user interface that allows users to input their education, experience, location, job role, and company size. Based on these inputs, the application predicts the salary of the user.

The web application also includes various visualizations and charts that help users understand how their salary compares to industry standards and what factors are influencing their earnings. The application also provides recommendations and suggestions on how users can improve their salary based on their input variables.

In conclusion, the salary prediction web application is a powerful tool that can help individuals understand the factors that influence their salary and how they can use this information to make informed decisions about their career path. The machine learning model used in this project is accurate and reliable, and the web application is user-friendly and easy to use. Overall, this project is a great example of how data science and machine learning can be used to solve real-world problems and help individuals achieve their professional goals.