Salary Prediction Project

This project aims to predict salaries based on various job-related features using a Random Forest Regressor model on a job dataset.

Project Overview

The dataset contains information about job titles, categories, experience levels, employment types, and more. The goal is to predict salaries by analyzing these factors.

Dataset Information

The dataset includes columns such as:

• work_year: Year of data record
• job_title: Title of the job
• job_category: Category of the job
• salary_currency: Currency used for salary
• salary: Salary in the local currency
• salary_in_usd: Salary converted to USD
• experience_level: Level of experience (Junior, Mid-level, Senior, etc.)
• employment_type: Type of employment
• company_location: Location of the company

Project Workflow

Data Preprocessing

• Load the job dataset from a CSV file.
• Clean the data by handling missing values and encoding categorical features using one-hot encoding.
• Split the data into features (X) and the target variable (y).

Model Building

• Split the data into training and testing sets (80% train, 20% test).
• Utilize a Random Forest Regressor model for predicting salaries.
• Train the model using the training data.
• Evaluate model performance using R-squared and Mean Squared Error (MSE).

Visualization

• Visualize temporal trends in salaries using line plots.
• Analyze salary distributions across job categories with box plots.
• Display scatter plots comparing actual vs. predicted salaries.
• Create histograms to illustrate the distribution of median house values.

Tools Used

• Python
• Pandas for data manipulation
• Matplotlib for data visualization
• Scikit-learn for machine learning modeling

Instructions

To run the project:

1. Clone or download this repository to your local machine.
2. Ensure you have Python installed along with necessary libraries (Pandas, Matplotlib, Scikit-learn).
3. Run the provided code in your Python environment to perform the salary prediction analysis.

License