Salary Prediction Model using Linear Regression

This project implements a Multiple Linear Regression model to automate salary estimations for an HR department. The model predicts a candidate's salary based on three key factors: experience, written test score, and personal interview score.

📌 Project Overview

The goal is to provide a data-driven approach to hiring by analyzing historical recruitment statistics. This helps ensure fair and consistent salary offerings for future candidates.

Features:

• experience: Professional background (handles both text and numeric formats).
• test_score: Score out of 10 from a technical written test.
• interview_score: Score out of 10 from the personal interview.
• salary($): The target variable the model aims to predict.

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🛠️ Tech Stack

• Language: Python
• Libraries: * Pandas: For data manipulation and cleaning.
• Scikit-Learn: For building and training the Linear Regression model.
• Word2Number: To convert text-based experience (e.g., "five") into integers.

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🚀 Workflow

The project follows a standard machine learning pipeline:

1. Data Cleaning: Handling missing values. Missing experience is filled with "zero," and missing test scores are filled with the column median.
2. Data Transformation: Converting word-based numbers into numerical data.
3. Model Training: Fitting a Linear Regression model to the historical data.
4. Prediction: Generating salary estimates for new candidate profiles.

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📊 Results

The model was tested with two sample candidates:

Candidate	Experience	Test Score	Interview Score	Predicted Salary
New Hire 1	2 Years	9 / 10	6 / 10	$53,205.97
New Hire 2	12 Years	10 / 10	10 / 10	$92,002.18

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💻 How to Run

1. Clone this repository.
2. Ensure you have hiring (1).csv in the same directory.
3. Install dependencies:

pip install pandas scikit-learn word2number

4. Run the Jupyter Notebook or Python script to see the results.

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