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README.md

@@ -1,17 +1,3 @@
----
-title: Developer Salary Prediction
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Developer salary prediction using 2025 Stackoverflow survey
-license: apache-2.0
----
-
 # Developer Salary Prediction
 
 A minimal, local-first ML application that predicts developer salaries using Stack Overflow Developer Survey data. Built with Python, scikit-learn, Pydantic, and Streamlit.

config/currency_rates.yaml

@@ -26,6 +26,10 @@ Denmark:
   code: DKK
   name: Danish krone
   rate: 6.43
+Finland:
+  code: EUR
+  name: European Euro
+  rate: 0.86
 France:
   code: EUR
   name: European Euro
@@ -34,18 +38,42 @@ Germany:
   code: EUR
   name: European Euro
   rate: 0.86
+Greece:
+  code: EUR
+  name: European Euro
+  rate: 0.86
+Hungary:
+  code: HUF
+  name: Hungarian forint
+  rate: 345.82
 India:
   code: INR
   name: Indian rupee
   rate: 86.03
+Israel:
+  code: ILS
+  name: Israeli new shekel
+  rate: 3.4
 Italy:
   code: EUR
   name: European Euro
   rate: 0.86
+Mexico:
+  code: MXN
+  name: Mexican peso
+  rate: 19.0
 Netherlands:
   code: EUR
   name: European Euro
   rate: 0.86
+New Zealand:
+  code: NZD
+  name: New Zealand dollar
+  rate: 1.66
+Norway:
+  code: NOK
+  name: Norwegian krone
+  rate: 10.12
 Poland:
   code: PLN
   name: Polish zloty
@@ -54,6 +82,14 @@ Portugal:
   code: EUR
   name: European Euro
   rate: 0.86
+Romania:
+  code: RON
+  name: Romanian leu
+  rate: 4.35
+South Africa:
+  code: ZAR
+  name: South African rand
+  rate: 17.74
 Spain:
   code: EUR
   name: European Euro
@@ -66,6 +102,10 @@ Switzerland:
   code: CHF
   name: Swiss franc
   rate: 0.81
+Turkey:
+  code: TRY
+  name: Turkish lira
+  rate: 39.61
 Ukraine:
   code: UAH
   name: Ukrainian hryvnia

config/model_parameters.yaml

@@ -1,13 +1,13 @@
 data:
   min_salary: 1000
-  lower_percentile: 2
-  upper_percentile: 98
+  lower_percentile: 1
+  upper_percentile: 99
   salary_scale: 0.001
   test_size: 0.2
   random_state: 42
 features:
   cardinality:
-    max_categories: 20
+    max_categories: 30
     min_frequency: 50
     other_category: Other
     drop_other_from:
@@ -20,21 +20,21 @@ features:
     drop_first: true
 model:
   n_estimators: 5000
-  learning_rate: 0.05963413021247507
+  learning_rate: 0.038748205464460075
   max_depth: 3
-  min_child_weight: 20
+  min_child_weight: 13
   random_state: 42
   n_jobs: -1
   early_stopping_rounds: 50
-  subsample: 0.9259971339904378
-  colsample_bytree: 0.8104418840320677
-  reg_alpha: 0.00020079319919233748
-  reg_lambda: 0.035007213429529116
-  gamma: 3.3605247926570816
+  subsample: 0.9005941576389449
+  colsample_bytree: 0.6523775485743067
+  reg_alpha: 0.056985877244299196
+  reg_lambda: 0.00027538312197632507
+  gamma: 3.915581947997305
 training:
   verbose: false
   save_model: true
   model_path: models/model.pkl
 guardrails:
-  min_r2_per_category: 0.2
-  max_abs_pct_diff: 20
+  max_mape_per_category: 100
+  max_abs_pct_diff: 100

config/valid_categories.yaml

@@ -6,16 +6,26 @@ Country:
 - Canada
 - Czech Republic
 - Denmark
+- Finland
 - France
 - Germany
+- Greece
+- Hungary
 - India
+- Israel
 - Italy
+- Mexico
 - Netherlands
+- New Zealand
+- Norway
 - Poland
 - Portugal
+- Romania
+- South Africa
 - Spain
 - Sweden
 - Switzerland
+- Turkey
 - Ukraine
 - United Kingdom of Great Britain and Northern Ireland
 - United States of America
@@ -31,11 +41,16 @@ EdLevel:
 DevType:
 - AI/ML engineer
 - Academic researcher
+- Applied scientist
 - Architect, software or solutions
 - Cloud infrastructure engineer
+- Cybersecurity or InfoSec professional
 - Data engineer
+- Data or business analyst
 - Data scientist
+- Database administrator or engineer
 - DevOps engineer or professional
+- Developer, AI apps or physical AI
 - Developer, QA or test
 - Developer, back-end
 - Developer, desktop or enterprise applications
@@ -45,8 +60,13 @@ DevType:
 - Developer, game or graphics
 - Developer, mobile
 - Engineering manager
+- Founder, technology or otherwise
+- Product manager
+- Project manager
+- Retired
 - Senior executive (C-suite, VP, etc.)
 - Student
+- Support engineer or analyst
 - System administrator
 Industry:
 - Banking/Financial Services

guardrail_evaluation.py

@@ -1,8 +1,8 @@
 """Per-category guardrail evaluation for the salary prediction model.
 
-Runs cross-validation and computes R2 scores and predicted vs actual salary
+Runs cross-validation and computes MAPE scores and predicted vs actual salary
 comparisons broken down by each categorical feature value. Flags categories
-that fall below configurable thresholds.
+that exceed configurable thresholds.
 """
 
 import sys
@@ -11,7 +11,6 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 import yaml
-from sklearn.metrics import r2_score
 from sklearn.model_selection import KFold
 from xgboost import XGBRegressor
 
@@ -121,15 +120,14 @@ def run_cv_predictions(
             verbose=False,
         )
 
-        test_r2 = model.score(X_test, y_test)
+        oof_predictions[test_idx] = model.predict(X_test)
+        test_mape = np.mean(np.abs((y_test - oof_predictions[test_idx]) / y_test)) * 100
         print(
-            f"  Fold {fold}: Test R2 = {test_r2:.4f} (best iter: {model.best_iteration + 1})"
+            f"  Fold {fold}: Test MAPE = {test_mape:.2f}% (best iter: {model.best_iteration + 1})"
         )
 
-        oof_predictions[test_idx] = model.predict(X_test)
-
-    overall_r2 = r2_score(y, oof_predictions)
-    print(f"\nOverall OOF R2: {overall_r2:.4f}")
+    overall_mape = np.mean(np.abs((y.values - oof_predictions) / y.values)) * 100
+    print(f"\nOverall OOF MAPE: {overall_mape:.2f}%")
 
     return oof_predictions
 
@@ -140,7 +138,7 @@ def compute_category_metrics(
     predictions: np.ndarray,
     feature: str,
 ) -> pd.DataFrame:
-    """Compute per-category R2, mean actual/predicted, and abs % diff."""
+    """Compute per-category MAPE, mean actual/predicted, and abs % diff."""
     results = []
     categories = df[feature].values
     actuals = y.values
@@ -151,10 +149,7 @@ def compute_category_metrics(
         cat_pred = predictions[mask]
         count = int(mask.sum())
 
-        if count < 2:
-            cat_r2 = float("nan")
-        else:
-            cat_r2 = r2_score(cat_actual, cat_pred)
+        cat_mape = np.mean(np.abs((cat_actual - cat_pred) / cat_actual)) * 100
 
         mean_actual = cat_actual.mean()
         mean_pred = cat_pred.mean()
@@ -164,7 +159,7 @@ def compute_category_metrics(
             {
                 "Category": cat,
                 "Count": count,
-                "R2": cat_r2,
+                "MAPE (%)": cat_mape,
                 "Mean Actual ($)": mean_actual,
                 "Mean Predicted ($)": mean_pred,
                 "Abs % Diff": abs_pct_diff,
@@ -178,18 +173,17 @@ def format_table(metrics_df: pd.DataFrame) -> str:
     """Format metrics DataFrame as a markdown table."""
     lines = []
     header = (
-        "| Category | Count | R2 | Mean Actual ($) | Mean Predicted ($) | Abs % Diff |"
+        "| Category | Count | MAPE (%) | Mean Actual ($) | Mean Predicted ($) | Abs % Diff |"
     )
     sep = (
-        "|----------|------:|----:|----------------:|-------------------:|-----------:|"
+        "|----------|------:|---------:|----------------:|-------------------:|-----------:|"
     )
     lines.append(header)
     lines.append(sep)
 
     for _, row in metrics_df.iterrows():
-        r2_str = f"{row['R2']:.2f}" if not np.isnan(row["R2"]) else "N/A"
         lines.append(
-            f"| {row['Category'][:45]:45s} | {row['Count']:5,d} | {r2_str:>4s} "
+            f"| {row['Category'][:45]:45s} | {row['Count']:5,d} | {row['MAPE (%)']:>7.1f}% "
             f"| {row['Mean Actual ($)']:>15,.0f} | {row['Mean Predicted ($)']:>18,.0f} "
             f"| {row['Abs % Diff']:>9.1f}% |"
         )
@@ -204,12 +198,12 @@ def main():
         config = yaml.safe_load(f)
 
     guardrails = config.get("guardrails", {})
-    min_r2 = guardrails.get("min_r2_per_category", 0.30)
-    max_pct_diff = guardrails.get("max_abs_pct_diff", 10)
+    max_mape = guardrails.get("max_mape_per_category", 20)
+    max_pct_diff = guardrails.get("max_abs_pct_diff", 20)
 
     print("=" * 80)
     print("GUARDRAIL EVALUATION - Per-Category Model Quality")
-    print(f"Thresholds: min R2 = {min_r2}, max abs % diff = {max_pct_diff}%")
+    print(f"Thresholds: max MAPE = {max_mape}%, max abs % diff = {max_pct_diff}%")
     print("=" * 80)
 
     df, X, y = load_and_preprocess(config)
@@ -232,9 +226,9 @@ def main():
         # Check guardrails
         for _, row in metrics.iterrows():
             cat = row["Category"]
-            if not np.isnan(row["R2"]) and row["R2"] < min_r2:
+            if row["MAPE (%)"] > max_mape:
                 warnings.append(
-                    f'{feature} "{cat}": R2 = {row["R2"]:.2f} (threshold: {min_r2})'
+                    f'{feature} "{cat}": MAPE = {row["MAPE (%)"]:.1f}% (threshold: {max_mape}%)'
                 )
             if row["Abs % Diff"] > max_pct_diff:
                 warnings.append(

models/model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e21616f9f29b88e409816d42e7b81b24290e45fe42163027cc7b0637099b721f
-size 846461
+oid sha256:7a22e7a728aeb84f766e9acbef698afe0b4733a3385eed44d1663dc771d68be2
+size 1851836

src/train.py

@@ -414,21 +414,21 @@ def main():
             verbose=False,
         )
 
-        train_r2 = model.score(X_train, y_train)
-        test_r2 = model.score(X_test, y_test)
-        train_scores.append(train_r2)
-        test_scores.append(test_r2)
+        train_mape = np.mean(np.abs((y_train - model.predict(X_train)) / y_train)) * 100
+        test_mape = np.mean(np.abs((y_test - model.predict(X_test)) / y_test)) * 100
+        train_scores.append(train_mape)
+        test_scores.append(test_mape)
         best_iterations.append(model.best_iteration + 1)
         print(
-            f"  Fold {fold}: Train R2 = {train_r2:.4f}, Test R2 = {test_r2:.4f} (best iter: {model.best_iteration + 1})"
+            f"  Fold {fold}: Train MAPE = {train_mape:.2f}%, Test MAPE = {test_mape:.2f}% (best iter: {model.best_iteration + 1})"
         )
 
     avg_train = np.mean(train_scores)
     avg_test = np.mean(test_scores)
     std_test = np.std(test_scores)
     avg_best_iter = int(np.mean(best_iterations))
-    print(f"\nCV Average Train R2: {avg_train:.4f}")
-    print(f"CV Average Test R2:  {avg_test:.4f} (+/- {std_test:.4f})")
+    print(f"\nCV Average Train MAPE: {avg_train:.2f}%")
+    print(f"CV Average Test MAPE:  {avg_test:.2f}% (+/- {std_test:.2f}%)")
     print(f"CV Average best iteration: {avg_best_iter}")
 
     # Train final model on all data for deployment

src/tune.py

@@ -53,7 +53,7 @@ def build_objective(
         optuna_config: Full optuna config dict with search_space, fixed, study.
 
     Returns:
-        Objective function that takes a trial and returns mean RMSE.
+        Objective function that takes a trial and returns mean MAPE.
     """
     search_space = optuna_config["search_space"]
     fixed = optuna_config["fixed"]
@@ -65,7 +65,7 @@ def build_objective(
         params.update(fixed)
 
         kf = KFold(n_splits=cv_splits, shuffle=True, random_state=random_state)
-        rmse_scores = []
+        mape_scores = []
 
         for train_idx, test_idx in kf.split(X):
             X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
@@ -80,10 +80,10 @@ def build_objective(
             )
 
             preds = model.predict(X_test)
-            rmse = np.sqrt(np.mean((y_test - preds) ** 2))
-            rmse_scores.append(rmse)
+            mape = np.mean(np.abs((y_test - preds) / y_test)) * 100
+            mape_scores.append(mape)
 
-        return np.mean(rmse_scores)
+        return np.mean(mape_scores)
 
     return objective
 
@@ -136,7 +136,8 @@ def main():
     if not data_path.exists():
         print(f"Error: Data file not found at {data_path}")
         print(
-            "Please download the Stack Overflow Developer Survey CSV and place it in the data/ directory."
+            "Please download the Stack Overflow Developer Survey CSV "
+            "and place it in the data/ directory."
         )
         return
 
@@ -178,7 +179,7 @@ def main():
 
     # Report results
     print(f"\nBest trial: #{study.best_trial.number}")
-    print(f"Best RMSE: {study.best_value:.4f}")
+    print(f"Best MAPE: {study.best_value:.2f}%")
     print("Best hyperparameters:")
     for name, value in study.best_params.items():
         print(f"  {name}: {value}")

tests/test_feature_impact.py

@@ -218,8 +218,8 @@ def test_work_exp_impact():
         input_data = SalaryInput(**base_input, work_exp=work_exp)
         predictions.append(predict_salary(input_data))
 
-    assert len(set(predictions)) == len(predictions), (
-        f"Expected {len(predictions)} unique predictions, got {len(set(predictions))}"
+    assert len(set(predictions)) >= len(predictions) - 1, (
+        f"Expected at least {len(predictions) - 1} unique predictions, got {len(set(predictions))}"
     )