Salary Prediction Website

Salary Prediction Web App

This project aims to predict software developer salaries in 2023 based on various factors such as country, education level, and years of coding experience. Please note that as a beginner in the field of machine learning, the accuracy of the model may not be optimal. However, I am committed to continuously improving the model as I gain more knowledge and experience in the field.

Features:

• Predict Page: Allows users to input their country, education level, and years of coding experience to get an estimated salary.
• Explore Page: Provides visualizations of salary distributions based on different countries and years of experience.

Technologies Used:

• Python
• Streamlit:For building the interactive web application.
• Pandas: For data manipulation and preprocessing.
• Scikit-learn: For machine learning model training and evaluation.
• Matplotlib and Seaborn:For data visualization.

Machine Learning Models
This project utilizes several machine learning models and techniques for predicting software developer salaries:

Decision Tree Regressor: Used to build a decision tree-based model for regression tasks. It partitions the feature space into regions and makes predictions based on the average target value within each region.

Random Forest Regressor: Employed as an ensemble learning technique to combine multiple decision trees and improve prediction accuracy. Each tree in the random forest is trained on a random subset of the data.

Linear Regression: Applied for modeling the relationship between independent variables (features) and the dependent variable (salary) using a linear equation.

GridSearchCV with Decision Tree Regressor: Utilized for hyperparameter tuning, GridSearchCV systematically searches for the optimal hyperparameters of the decision tree regressor, such as the maximum depth of the tree.