https://github.com/pngo1997/predictive-model-data-science-salaries

Project analyzes and predicts Data Science salaries worldwide (2020-2023) using Multiple Linear Regression.
https://github.com/pngo1997/predictive-model-data-science-salaries

eda multiple-linear-regression predictive-analytics predictive-modeling

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Project analyzes and predicts Data Science salaries worldwide (2020-2023) using Multiple Linear Regression.

• Host: GitHub
• URL: https://github.com/pngo1997/predictive-model-data-science-salaries
• Owner: pngo1997
• Created: 2025-01-30T18:11:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-30T18:24:50.000Z (over 1 year ago)
• Last Synced: 2025-01-30T19:32:28.944Z (over 1 year ago)
• Topics: eda, multiple-linear-regression, predictive-analytics, predictive-modeling
• Language: SAS
• Homepage:
• Size: 8.33 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🏗️ Data Science Salaries Prediction  

## 📜 Overview  

This project analyzes and predicts **Data Science salaries worldwide (2020-2023)** using **Multiple Linear Regression**. The dataset contains **3,755 observations** from Kaggle, including job details such as **experience level, employment type, salary, company location, and remote work ratio**. The goal is to develop a predictive model that estimates **future Data Science salaries** based on employment attributes.  

## 🎯 Problem Explanation  

The dataset includes **11 attributes** (4 numerical and 7 categorical):  

- **Target Variable:** `salary_in_usd` (Salary in USD).  

- **Independent Variables:**  

  - `work_year` (Year salary was paid).  

  - `experience_level` (Entry, Mid, Senior, Executive).  

  - `employment_type` (Part-time, Full-time, Contract, Freelance).  

  - `job_title` (Data Scientist, Engineer, etc.).  

  - `salary` (Salary in original currency).  

  - `salary_currency` (USD, EUR, GBP, etc.).  

  - `employee_residence` (Country of employee residence).  

  - `remote_ratio` (0 = No remote, 50 = Hybrid, 100 = Fully remote).  

  - `company_location` (Employer's country).  

  - `company_size` (S = <50, M = 50-250, L = >250 employees).  

## 🛠️ Implementation Details  

- **Exploratory Data Analysis (EDA):**  

  - Applied **square root transformation** to normalize salary distribution.  

  - Created **dummy variables** for categorical attributes.  

  - Analyzed **correlations & multicollinearity (VIF test)**.  

- **Regression Models:**  

  - **Full Model:** All predictors included (Adjusted R² = 39.34%).  

  - **Refined Model (Removing Multicollinearity):**  

    - Excluded `company_location` due to high correlation with `employee_residence`.  

    - Improved Adjusted R² to **39.35%**.  

  - **Stepwise Selection Model:**  

    - Reduced to **six key predictors** (Adjusted R² = **39.46%**).  

  - **Final Model (After Outlier Removal):**  

    - Adjusted R² = **41.84%**, RMSE = **64.73**, F-value = **440.04**, P-value < **0.0001**.  

- **Hypothesis Testing (F-Test):**  

  - Null Hypothesis: None of the six predictors significantly impact salary.  

  - Alternative Hypothesis: At least one predictor has a significant impact.  

  - Result: **Rejected Null Hypothesis**, confirming predictor relevance.