📈 Salary Prediction Model – Job Market Prep with Linear Regression

• This project uses a simple linear regression model to predict salary based on years of experience in the technology sector, using real-world data from California, Texas, and New York.

💡 Why This Matters

• I built this model to help friends as part of their job search to benchmark salary expectations and make data-informed decisions. While the model is simple, the goal is serious: use data to drive better career and compensation strategy.

• 🔍 Sometimes, it's not the complexity of the model that matters—but its clarity, purpose, and precision.

⚙️ Methodology

• Tool: Google Sheets (regression formula & visualization)

• Input: Public salary datasets from tech roles

• Output: A predicted salary based on years of experience

• Format: CSV and visual chart

• Target: Junior Mid and Senior-level roles

🧠 Use Case

• This model was developed to:

• Guide salary negotiation conversations

• Anchor expectations for senior tech roles in FinTech, AI, and product

• Show how even basic statistical models can support real decisions

🚀 Key Insight

What is Linear Regression?

• Linear regression is a statistical method that models the relationship between variables by fitting a straight line through the data points. This technique assumes a linear relationship between the input features and the target variable, making it ideal for predicting continuous outcomes like salary ranges.

• The model predicts values along a continuous scale, meaning the output can be any numerical value within the range of possibilities. Linear regression uses Ordinary Least Squares (OLS) as its optimization method, which finds the best-fitting line by minimizing the sum of squared differences between predicted and actual values.

• This straightforward yet powerful approach makes linear regression an excellent starting point for understanding predictive modeling and serves as a valuable tool for salary estimation based on relevant job market factors.