Salary Prediction using Support Vector Regression (SVR)

This project implements Support Vector Regression (SVR) to predict the salary of an employee based on their position level. The script uses a dataset that contains position levels and corresponding salaries, applying feature scaling to improve the performance of the SVR model. The results are visualized to show how well the model fits the data.

Detailed Summary

The script loads the Position_Salaries.csv dataset, containing employee position levels and their respective salaries. Support Vector Regression is trained on the dataset using the Gaussian Radial Basis Function (RBF) Kernel, which is ideal for capturing non-linear relationships in the data. The script applies feature scaling to both the input features (position levels) and the target variable (salaries) before training the SVR model.

The script performs the following steps:

1. Data Preprocessing:

   • Loads the dataset and separates it into independent variable (position level) and dependent variable (salary).
   • Reshapes the target variable y to a 2D array to ensure compatibility with the scaling process.

2. Feature Scaling:

   • Applies StandardScaler to scale the features and target variable, which is crucial for SVR to perform optimally as it is sensitive to the range of input data.

3. SVR Model Training:

   • Trains the SVR model with a Radial Basis Function (RBF) kernel on the scaled data.

4. Prediction:

   • Makes predictions on new data points (position level 6.5) by reversing the scaling transformation to interpret the predicted values in the original salary scale.

5. Visualization:

   • The script visualizes the SVR results by plotting both the original and predicted salaries, showing the fit of the model to the dataset.
   • A higher resolution plot is also generated to display a smoother curve that captures the non-linearity of the data.

Key Concepts Covered

• Support Vector Regression (SVR): A powerful regression technique that uses support vectors to model complex relationships between features and target variables, particularly effective with non-linear data.
• Gaussian Radial Basis Function (RBF) Kernel: A kernel function used in SVR that maps inputs into a higher-dimensional space, allowing the model to capture non-linear relationships between position levels and salaries.
• Feature Scaling: Applies StandardScaler to both the independent and dependent variables to ensure that the SVR model performs effectively, as SVR is sensitive to the range of the data.
• Inverse Transformation: After training the model on scaled data, the script reverses the scaling transformations to present predictions in the original scale of the dataset.
• Data Visualization: Visualizes the performance of the SVR model and provides insights into how well it fits the data compared to the actual salary values.

How to Run

1. Install required dependencies:

   pip install numpy pandas matplotlib scikit-learn