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GowthamD03/
attritionmodel

Name: attritionmodel
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Attrition Prediction — Model B (XGBoost + Feature Engineering)

A binary classifier that predicts employee attrition risk using XGBoost trained on 14 engineered features derived from 8 raw HR signals.

Model at a Glance

Property	Value
Algorithm	XGBoost + StandardScaler (sklearn Pipeline)
Feature Set	14 (8 raw + 6 engineered)
Cross-Val F1	0.9834 ± 0.0008
Cross-Val AUC	0.9996 ± 0.0000
Cross-Val Recall	0.9800 ± 0.0008
Cross-Val Precision	0.9869 ± 0.0010
Test AUC	0.9997
Bundle file	`attrition_model_bundle_XGB_FE.pkl`

Features

Raw Inputs (8)

Feature	Range	Description
`EngagementScore`	0–10	Employee engagement level
`TenureMonths`	1–120	Months of employment
`StressScore`	0–10	Workplace stress level
`PromotionDelay`	0–120	Months since last promotion
`EngagementTrend`	−1 – +1	Direction of recent engagement change
`SentimentScore`	−1 – +1	Survey/text-derived sentiment
`PerformanceScore`	0–10	Manager-rated performance
`ConflictScore`	0–10	Frequency/severity of workplace conflicts

Engineered Features (6)

Feature	Formula	Rationale
`stress_per_tenure`	`StressScore / (TenureMonths + 1)`	Stress normalised by tenure; newer employees under high stress are highest-risk
`engagement_drop`	`EngagementScore * (1 - EngagementTrend)`	Combines current engagement level with rate of decline
`burnout_index`	`(StressScore * ConflictScore) / (EngagementScore + 1)`	Non-linear burnout composite; amplifies when stress and conflict are high and engagement is low
`stress_log`	`log1p(StressScore)`	Log-transforms stress to compress the tail and improve split quality
`tenure_sqrt`	`sqrt(TenureMonths)`	Captures diminishing tenure effect on attrition risk
`fe_risk_score`	`0.4 * Stress + 0.3 * Conflict - 0.3 * Engagement`	Domain-expert weighted composite; directly interpretable by HR teams

Hyperparameters

python

XGBClassifier(
    n_estimators=400,
    max_depth=5,
    learning_rate=0.05,
    subsample=0.85,
    colsample_bytree=0.85,
    eval_metric="logloss",
    random_state=42
)

Pipeline: StandardScaler → XGBClassifier

Installation

pip install xgboost scikit-learn joblib pandas numpy

Usage

Load the bundle

python

import joblib
import pandas as pd
import numpy as np

bundle = joblib.load("attrition_model_bundle_XGB_FE.pkl")
pipeline   = bundle["pipeline"]
features   = bundle["features"]          # 14-element list
add_features = bundle["feature_engineering"]  # callable
thresholds = bundle["risk_thresholds"]   # {"high": 0.60, "medium": 0.35}

Feature engineering function

python

def add_features(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    df["stress_per_tenure"] = df["StressScore"] / (df["TenureMonths"] + 1)
    df["engagement_drop"]   = df["EngagementScore"] * (1 - df["EngagementTrend"])
    df["burnout_index"]     = (df["StressScore"] * df["ConflictScore"]) / (df["EngagementScore"] + 1)
    df["stress_log"]        = np.log1p(df["StressScore"])
    df["tenure_sqrt"]       = np.sqrt(df["TenureMonths"])
    df["fe_risk_score"]     = (
        0.4 * df["StressScore"]
        + 0.3 * df["ConflictScore"]
        - 0.3 * df["EngagementScore"]
    )
    return df

Predict a single employee

python

RAW_FEATURES = [
    "EngagementScore", "TenureMonths", "StressScore", "PromotionDelay",
    "EngagementTrend", "SentimentScore", "PerformanceScore", "ConflictScore"
]

def predict(raw_input: dict) -> dict:
    row = pd.DataFrame([raw_input])[RAW_FEATURES]
    row = add_features(row)[features]

    prob = pipeline.predict_proba(row)[0, 1]
    prob = np.clip(prob, 0.05, 0.95)

    tier = "High" if prob >= 0.60 else "Medium" if prob >= 0.35 else "Low"

    return {
        "resign_pct": round(prob * 100, 2),
        "stay_pct":   round((1 - prob) * 100, 2),
        "risk_tier":  tier,
        "probability": round(prob, 4),
    }

# Example
result = predict({
    "EngagementScore": 3.2,
    "TenureMonths": 28,
    "StressScore": 8.7,
    "PromotionDelay": 28,
    "EngagementTrend": -0.62,
    "SentimentScore": -0.30,
    "PerformanceScore": 6.8,
    "ConflictScore": 6.2,
})
print(result)
# {'resign_pct': 95.0, 'stay_pct': 5.0, 'risk_tier': 'High', 'probability': 0.95}

Batch prediction

python

df_raw = pd.read_csv("employees.csv")
df_fe  = add_features(df_raw)[features]

probs = pipeline.predict_proba(df_fe)[:, 1]
probs = np.clip(probs, 0.05, 0.95)

df_raw["resign_pct"] = (probs * 100).round(2)
df_raw["risk_tier"]  = pd.cut(
    probs,
    bins=[0, 0.35, 0.60, 1.0],
    labels=["Low", "Medium", "High"]
)

Risk Tiers

Tier	Probability Threshold	Recommended Action
🔴 High	≥ 60%	Immediate manager intervention + retention package
🟡 Medium	35% – 59%	Scheduled 1:1 check-in + engagement survey
🟢 Low	< 35%	Standard monitoring cadence

SHAP Explainability

python

import shap

scaler    = pipeline.named_steps["scaler"]
xgb_model = pipeline.named_steps["xgb"]

# Scale with feature names preserved — required for correct SHAP labels
X_scaled = pd.DataFrame(
    scaler.transform(X_sample),
    columns=X_sample.columns
)

# Background sample for interventional perturbation
background = pd.DataFrame(
    scaler.transform(X_train.sample(100, random_state=42)),
    columns=X_train.columns
)

explainer   = shap.TreeExplainer(xgb_model, background, feature_perturbation="interventional")
shap_values = explainer(X_scaled)

shap.plots.beeswarm(shap_values, max_display=14)

Important: always pass the scaled feature matrix to TreeExplainer, not raw values. Wrap the scaled array in a named DataFrame to preserve feature labels in plots.

Bundle Contents

python

{
    "pipeline":            <sklearn Pipeline>,  # StandardScaler + XGBClassifier
    "features":            [...],               # ordered list of 14 feature names
    "feature_engineering": <callable>,          # add_features() function
    "model_type":          "stack_xgb_fe",
    "risk_thresholds":     {"high": 0.60, "medium": 0.35},
    "shap_note":           "Scale before SHAP; wrap in named DataFrame"
}

Notes

Probabilities are clipped to [0.05, 0.95] to prevent degenerate 0% / 100% outputs on edge-case inputs.
The fe_risk_score feature is a useful standalone KPI for HR dashboards — it requires no model inference.
For highest accuracy at the cost of inference speed, use Model C (stacking ensemble). Model B is recommended for real-time API deployments.
Re-train quarterly on updated HR data to account for organisational changes.

GowthamD03/attritionmodel

Attrition Prediction — Model B (XGBoost + Feature Engineering)

Model at a Glance

Features

Raw Inputs (8)

Engineered Features (6)

Hyperparameters

Installation

Usage

Load the bundle

Feature engineering function

Predict a single employee

Batch prediction

Risk Tiers

SHAP Explainability

Bundle Contents

Notes

GowthamD03/
attritionmodel