{"id":25819045,"url":"https://github.com/pngo1997/predictive-model-data-science-salaries","last_synced_at":"2026-07-12T13:31:40.570Z","repository":{"id":275025193,"uuid":"924827694","full_name":"pngo1997/Predictive-Model-Data-Science-Salaries","owner":"pngo1997","description":"Project analyzes and predicts Data Science salaries worldwide (2020-2023) using Multiple Linear Regression.","archived":false,"fork":false,"pushed_at":"2025-01-30T18:24:50.000Z","size":8738,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-25T20:48:32.517Z","etag":null,"topics":["eda","multiple-linear-regression","predictive-analytics","predictive-modeling","r"],"latest_commit_sha":null,"homepage":"","language":"SAS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pngo1997.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-30T18:11:30.000Z","updated_at":"2025-01-30T19:34:56.000Z","dependencies_parsed_at":"2025-01-30T19:42:43.177Z","dependency_job_id":null,"html_url":"https://github.com/pngo1997/Predictive-Model-Data-Science-Salaries","commit_stats":null,"previous_names":["pngo1997/predictive-model-data-science-salaries"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pngo1997/Predictive-Model-Data-Science-Salaries","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pngo1997%2FPredictive-Model-Data-Science-Salaries","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pngo1997%2FPredictive-Model-Data-Science-Salaries/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pngo1997%2FPredictive-Model-Data-Science-Salaries/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pngo1997%2FPredictive-Model-Data-Science-Salaries/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pngo1997","download_url":"https://codeload.github.com/pngo1997/Predictive-Model-Data-Science-Salaries/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pngo1997%2FPredictive-Model-Data-Science-Salaries/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35393398,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-12T02:00:06.386Z","response_time":87,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eda","multiple-linear-regression","predictive-analytics","predictive-modeling","r"],"created_at":"2025-02-28T08:14:28.248Z","updated_at":"2026-07-12T13:31:40.536Z","avatar_url":"https://github.com/pngo1997.png","language":"SAS","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🏗️ Data Science Salaries Prediction  \n\n## 📜 Overview  \nThis 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.  \n\n## 🎯 Problem Explanation  \nThe dataset includes **11 attributes** (4 numerical and 7 categorical):  \n- **Target Variable:** `salary_in_usd` (Salary in USD).  \n- **Independent Variables:**  \n  - `work_year` (Year salary was paid).  \n  - `experience_level` (Entry, Mid, Senior, Executive).  \n  - `employment_type` (Part-time, Full-time, Contract, Freelance).  \n  - `job_title` (Data Scientist, Engineer, etc.).  \n  - `salary` (Salary in original currency).  \n  - `salary_currency` (USD, EUR, GBP, etc.).  \n  - `employee_residence` (Country of employee residence).  \n  - `remote_ratio` (0 = No remote, 50 = Hybrid, 100 = Fully remote).  \n  - `company_location` (Employer's country).  \n  - `company_size` (S = \u003c50, M = 50-250, L = \u003e250 employees).  \n\n## 🛠️ Implementation Details  \n- **Exploratory Data Analysis (EDA):**  \n  - Applied **square root transformation** to normalize salary distribution.  \n  - Created **dummy variables** for categorical attributes.  \n  - Analyzed **correlations \u0026 multicollinearity (VIF test)**.  \n- **Regression Models:**  \n  - **Full Model:** All predictors included (Adjusted R² = 39.34%).  \n  - **Refined Model (Removing Multicollinearity):**  \n    - Excluded `company_location` due to high correlation with `employee_residence`.  \n    - Improved Adjusted R² to **39.35%**.  \n  - **Stepwise Selection Model:**  \n    - Reduced to **six key predictors** (Adjusted R² = **39.46%**).  \n  - **Final Model (After Outlier Removal):**  \n    - Adjusted R² = **41.84%**, RMSE = **64.73**, F-value = **440.04**, P-value \u003c **0.0001**.  \n- **Hypothesis Testing (F-Test):**  \n  - Null Hypothesis: None of the six predictors significantly impact salary.  \n  - Alternative Hypothesis: At least one predictor has a significant impact.  \n  - Result: **Rejected Null Hypothesis**, confirming predictor relevance.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpngo1997%2Fpredictive-model-data-science-salaries","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpngo1997%2Fpredictive-model-data-science-salaries","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpngo1997%2Fpredictive-model-data-science-salaries/lists"}