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https://github.com/ebadshabbir/bluff-detection-model-polynomial_regression-

This project predicts employee salaries based on position levels using Linear and Polynomial Regression models. It trains models with degrees 2, 3, and 4 on a dataset of job titles, position levels, and salaries, and predicts the salary for a position level of 6.5. The results are visualized to compare model performance.
https://github.com/ebadshabbir/bluff-detection-model-polynomial_regression-

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This project predicts employee salaries based on position levels using Linear and Polynomial Regression models. It trains models with degrees 2, 3, and 4 on a dataset of job titles, position levels, and salaries, and predicts the salary for a position level of 6.5. The results are visualized to compare model performance.

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README 

        
# Polynomial and Linear Regression for Position Salaries



This project uses both Linear and Polynomial Regression techniques to predict employee salaries based on their job position levels. The dataset contains position levels and their corresponding salaries, allowing us to model and compare different degrees of polynomial regression.



## Table of Contents

- [Overview](#overview)

- [Dependencies](#dependencies)

- [Installation](#installation)

- [Dataset](#dataset)

- [Usage](#usage)

- [Model Results](#model-results)

- [Visualizations](#visualizations)

- [License](#license)



## Overview

The purpose of this project is to:

- Train and visualize a **Linear Regression** model.

- Train and visualize **Polynomial Regression** models with degrees 2, 3, and 4.

- Compare the predictions made by each model for a position level of 6.5.



This allows us to understand the difference in performance between linear and polynomial regression when fitting non-linear data.



## Dependencies

The project uses the following Python libraries:

- `pandas`

- `scikit-learn`

- `matplotlib`



## Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/EbadShabbir/polynomial-regression-position-salaries.git

2. Installing Libraries:

   - pip install pandas scikit-learn matplotlib

   - pip install -r requirements.txt

# LINEAR REGRESSION RESULT

lin_pred = lin_regs.predict([[6.5]])  

print(f"Linear Regression Prediction for 6.5: {lin_pred}")



# POLYNOMIAL REGRESSION RESULT (degree 2)

poly_pred_2 = lin_reg_2.predict(poly_regs_2.fit_transform([[6.5]]))  

print(f"Polynomial Regression (Degree 2) Prediction for 6.5: {poly_pred_2}")



# POLYNOMIAL REGRESSION RESULT (degree 3)

poly_pred_3 = lin_reg_3.predict(poly_regs_3.fit_transform([[6.5]]))  

print(f"Polynomial Regression (Degree 3) Prediction for 6.5: {poly_pred_3}")



# POLYNOMIAL REGRESSION RESULT (degree 4)

poly_pred_4 = lin_reg_4.predict(poly_regs_4.fit_transform([[6.5]]))  

print(f"Polynomial Regression (Degree 4) Prediction for 6.5: {poly_pred_4}")



### Notes:

1. Make sure you include images of the regression plots (`linear_regression.png`, `polynomial_regression_2.png`, etc.) in the `images` directory.

2. Customize the actual predicted salary values in the `Model Results` section after running the script.

3. Ensure that the dataset path and other links (e.g., GitHub repository URL) are updated to reflect your environment.



This `README.md` should provide clear instructions and detailed explanations for users to understand and run your project.