Clipped extreme outliers at the 1st and 99th percentile (final median salary ≈ $75K)
Removed 44 columns with >60% missing values (170 → 126 columns), protecting the top-15 salary correlates regardless of missingness
2.2 Missing Value Analysis
01_Bar_chart_top-40_columns_by_missing
Several columns exceed 60% missingness and are dropped. The protected essential columns are retained despite high missingness and imputed later.
2.3 Descriptive Statistics
Statistic
Value
Median salary
~$75,000
Distribution
Right-skewed
Outlier treatment
1st–99th percentile clip
2.4 Salary Distribution
02_Distribution_of_Annual_Developer_Salary
The raw distribution is heavily right-skewed with a long tail above $200K.
2.5 Research Questions & Findings
Q1: Does coding experience predict salary?
03_Does_Coding_Experience_Predict_Salary
Salary increases steeply through the first 15–20 years of experience then flattens. There is wide variance at every experience level, suggesting experience alone is not sufficient to predict salary.
Q2: How does education level affect salary?
04_How_Does_Education_Level_Affect_Salary
Median salary rises with education level, but the gap between a Bachelor's and Master's degree is smaller than expected. Professional degrees and doctoral holders show the highest median salaries.
Q3: Which countries pay developers the most?
05_Which_Countries_Pay_Developers_the_Most
The US dominates with a median salary roughly 2–3× the global median. Israeli, Western European and Australian developers cluster in a second tier, while developers in Asia and South America earn less.
Q4: Do remote workers earn more?
06_Do_Remote_Workers_Earn_More
Fully remote developers show a slight salary premium over hybrid and in-office roles. The difference is modest, suggesting remote work correlates with higher-paying companies rather than being a direct cause.
Q5: How does salary vary across developer roles?
07_How_Does_Salary_Vary_Across_Developer_Roles
C-Suite and ML/Data Science roles have the widest salary ranges and highest medians. Full-stack and front-end developers cluster around the global median with less variance.
Salary is right-skewed; median ~$75K after cleaning
Work experience (WorkExp) and coding experience (YearsCode) are the strongest numeric predictors
Country is the dominant signal — geography explains more variance than any other feature
Remote work carries a small positive premium
Developer role and education have meaningful but secondary effects
Part 3 – Baseline Model
A simple Linear Regression trained on raw numeric columns only — no encoding, no feature engineering.
Train/Test Split
80/20 random split, SEED=42
Results
Metric
Baseline
MAE
$45,810
RMSE
$61,947
R²
0.1598
Predicted vs. Actual
08_Plot_1_Predicted_vs_Actual
The baseline model struggles with high earners — predictions cluster around the mean and fail to capture the upper salary range. The scatter is wide, consistent with an R² of only 0.16.
Binary flag for each of the top-10 programming languages
4.5 Imputation & scaling
Median imputation + StandardScaler → 249 features
KMeans Elbow Method
09_46_KMeans_Elbow
The inertia curve decreases gradually without a sharp elbow, reflecting the high-dimensional and overlapping nature of the data. k=4 was selected as a reasonable balance between cluster granularity and interpretability.
Silhouette scores are low across all values of k, confirming that natural cluster separation is weak in this dataset. Agglomerative clustering consistently outperforms KMeans, peaking around k=4.
Three Clustering Algorithms
Algorithm
k / params
Silhouette
KMeans
k=4
0.0109
DBSCAN
eps=5, min_samples=25
0.0912 (7 clusters)
Agglomerative Ward
k=4
0.0224
The data's high dimensionality (249 features) makes density-based clustering (DBSCAN) impractical — inter-point distances are too large for meaningful core-point detection.
Cluster Visualisations (PCA 2D)
11_48_Separate_scatter_plots
KMeans splits the data into four roughly equal blobs with significant overlap in the PCA projection. The clusters correspond loosely to salary level but boundaries are indistinct.
12_48_Separate_scatter_plots
DBSCAN classifies the vast majority of points as noise, forming 7 clusters. High dimensionality makes distance-based density estimation ineffective on this dataset.
13_48_Separate_scatter_plots
Agglomerative clustering produces the clearest separation, isolating a distinct high-salary cluster on the right of the PCA plot. The four tiers align visually with low, mainstream, high-mid, and elite salary groups.
Cluster Profiles – Agglomerative (Chosen)
Cluster
Mean Salary
Median Salary
Count
0
$86,041
$74,000
18,192
1
$109,980
$95,000
4,069
2
$27,574
$13,949
877
3
$101,735
$93,387
317
Winner: Agglomerative Ward (k=4) — highest silhouette score and four interpretable salary tiers (low-income, mainstream, high-mid, elite).
Cluster Feature Added
cluster_id one-hot encoded and appended → 253 final features
Part 5 – Improved Regression Models
Three models trained on the full 253-feature matrix (249 engineered features + 4 cluster dummies).
Results
Model
MAE
RMSE
R²
Baseline Linear Regression
$45,810
$61,947
0.1598
Improved Linear Regression
$30,688
$44,314
0.5701
Random Forest (200 trees)
$31,998
$45,784
0.5411
HistGradientBoosting (300 iters)
$28,991
$43,039
0.5944
Model Performance Comparison
14_Comparison_table
HistGradientBoosting wins on all three metrics. The jump from baseline to improved linear regression is dramatic — encoding Country alone accounts for the majority of the R² improvement from 0.16 to 0.57.
Feature Importance
15_Feature_importance_for_all_three_models
Country dummies (especially US) dominate feature importance across all three models. Work experience and years of coding rank consistently high. The cluster feature appears in the top 20 for linear regression, validating the clustering step.
The HistGradientBoosting model tracks the perfect-prediction diagonal much more closely than the baseline. It still under-predicts some very high earners above $300K but captures the mid-range salary distribution well.
Discussion
Baseline → Improved Linear Regression (+0.41 R²): One-hot encoding Country was the single biggest improvement. Geography is the dominant salary signal.
Random Forest vs. Linear Regression: Non-linear feature interactions (e.g. senior developer × US location) are captured naturally by trees.
HistGradientBoosting wins: Sequential boosting focuses on the hardest predictions. Natively handles missing values and is 10–100× faster than standard GradientBoosting.
Cluster feature: Pre-computed salary-tier signal from Part 4 particularly boosts Linear Regression.
Winner: HistGradientBoosting Regressor
Metric
Value
MAE
$28,991
RMSE
$43,039
R²
0.5944
Part 6 – Winning Regression Model Export
The winning regression model is saved to winning_model_regression.pkl.
Part 7 – Salary Classification Setup
The continuous salary target is binned into four ordered classes:
Class
Label
Range (USD/year)
0
Low
< $30,000
1
Mid
$30,000 – $90,000
2
High
$90,000 – $160,000
3
Very High
> $160,000
Class Distribution
17_Bar_chart
Mid-salary developers make up nearly 40% of the dataset. Very High earners are the smallest class at 13.6%, creating a mild class imbalance that the models must handle.
Salary Distribution per Class
18_Salary_Distribution_per_Class
Each bin shows a clean salary range with minimal overlap at the boundaries, confirming the thresholds were well-chosen. The Very High class has the widest spread, reflecting high variability among top earners.
Part 8 – Classification Models
Same 253-feature matrix as regression, with a stratified 80/20 train/test split.
Precision vs. Recall & False Positives vs. False Negatives
Recall is prioritised over precision in this task. Misclassifying a developer into a lower salary tier (a false negative) carries real-world cost — under-negotiation, poor benchmarking, missed career leverage — whereas a false positive (over-predicting a tier) is relatively benign.
False Negatives are more critical than False Positives. Predicting "Mid" when a developer is truly "High" or "Very High" obscures their earning potential. For this reason, evaluation uses weighted F1-score, which balances precision and recall across all four classes with particular attention to recall in the minority tiers (Low and Very High).
Results
Model
Accuracy
F1 (weighted)
Logistic Regression
0.598
0.597
Random Forest (200 trees)
0.605
0.597
HistGradientBoosting (300 iters)
0.611
0.610
Classification Model Comparison
19_Summary_table
HistGradientBoosting leads on both accuracy and weighted F1, though the margin between all three models is narrow. The gap is larger on F1, reflecting better handling of the minority classes.
Confusion Matrices
20_graph
All three models struggle most with the High class ($90K–$160K), frequently confusing it with Mid. HistGradientBoosting shows the best recall on the Low and Very High tiers — the most actionable classes — with misclassifications mostly occurring between adjacent salary bands.
Sequential boosting handles the sparse one-hot encoded feature space well, focuses capacity on the most difficult salary boundaries, and outperforms both Logistic Regression and Random Forest on accuracy and F1.
