Regression-Models-for-Salary-Prediction

In this repository, we will investigate various regression models to predict whether an individual being interviewed is misrepresenting their salary.

Project Title: Salary Prediction using Regression Models

Project Overview:

The "Salary Prediction using Regression Models" project aims to develop a predictive model to estimate salaries based on position levels. This project utilizes various regression algorithms, including Decision Tree Regression, Polynomial Regression, Support Vector Regression, and Random Forest Regression. By exploring multiple regression techniques, this project seeks to provide accurate salary predictions and valuable insights for salary negotiation and decision-making processes.

Key Objectives:

Data Analysis: Load and analyze the dataset containing position levels and corresponding salaries to gain insights into the relationship between the variables. Model Building: Implement Decision Tree, Polynomial, Support Vector, and Random Forest Regression models using scikit-learn to capture different patterns within the data. Model Training: Train each regression model using the provided dataset to learn the intricate relationships between position levels and salaries. Prediction: Utilize the trained models to predict salaries for given position levels, enabling well-informed salary estimates. Visualization: Visualize the original data points along with the regression lines to understand each model's predictions visually.

Technical Details:

Dataset: The project employs the "Position_Salaries.csv" dataset, which includes position levels and corresponding salaries. Libraries Used:

• NumPy: For numerical computations and array manipulation.
• pandas: For data loading, manipulation, and analysis.
• Matplotlib: For data visualization and plotting.
• scikit-learn: For building, training, and evaluating regression models.

Implementation Steps:

1. Data Loading: Load the dataset using pandas to access position levels and salaries.
2. Data Preprocessing: Perform any necessary data cleaning and preparation.
3. Training data on regression models: Decision Tree Regression: Implement a Decision Tree Regression model using scikit-learn. Polynomial Regression: Develop a Polynomial Regression model to capture non-linear relationships. Support Vector Regression: Build a Support Vector Regression model to handle complex patterns. Random Forest Regression: Create a Random Forest Regression model for improved prediction accuracy.
4. Model Training and Evaluation: Train each model using the dataset and evaluate their performance.
5. Prediction and Visualization: Use the trained models to make predictions and visualize the results.

Conclusion:

The "Salary Prediction using Regression Models" project demonstrates the application of various regression algorithms to predict salaries based on position levels. By employing different regression techniques, users can gain a comprehensive understanding of the underlying patterns and relationships within the data. This approach provides accurate salary predictions and valuable insights, aiding both job seekers and employers in making informed decisions during salary negotiations. Real-world platforms implementing similar techniques include Payscale and Glassdoor.