# app.py import numpy as np import pandas as pd import streamlit as st import joblib from pathlib import Path st.set_page_config(page_title="Employee Attrition Predictor (XGBoost)", page_icon="🏢", layout="centered") BASE_DIR = Path(__file__).resolve().parent MODEL_PATH = BASE_DIR / "xgb_model.pkl" FEATURES_PATH = BASE_DIR / "feature_names.pkl" THRESHOLD_PATH = BASE_DIR / "threshold.pkl" # --------- Load artifacts --------- @st.cache_resource def load_artifacts(): missing = [p.name for p in [MODEL_PATH, FEATURES_PATH, THRESHOLD_PATH] if not p.exists()] if missing: raise FileNotFoundError( f"Missing files: {missing}. Put them in the repo root (same folder as app.py)." ) model = joblib.load(MODEL_PATH) feature_names = joblib.load(FEATURES_PATH) threshold = joblib.load(THRESHOLD_PATH) # Safety if not isinstance(feature_names, (list, tuple)) or len(feature_names) == 0: raise ValueError("feature_names.pkl must be a non-empty list of column names.") threshold = float(threshold) return model, list(feature_names), threshold model, feature_names, threshold = load_artifacts() st.title("🏢 Employee Attrition Predictor (XGBoost)") st.caption("Predicts the probability that an employee will leave (Attrition=1).") with st.expander("Model info"): st.write(f"**Model:** XGBoost (saved as `xgb_model.pkl`)") st.write(f"**Number of features:** {len(feature_names)}") st.write(f"**Decision threshold:** {threshold:.2f}") st.write("Tip: Probability is used for Kaggle submissions (ROC-AUC metric).") # --------- Helpers --------- def build_input_from_form(form_values: dict) -> pd.DataFrame: """ Create a single-row dataframe aligned to training feature order. Any missing one-hot columns are filled with 0. """ X = pd.DataFrame([form_values]) X = X.reindex(columns=feature_names, fill_value=0) return X def predict_single(X_one_row: pd.DataFrame): proba = float(model.predict_proba(X_one_row)[:, 1][0]) pred = int(proba >= threshold) return pred, proba # --------- Input mode selection --------- mode = st.radio("Choose input method", ["Single prediction (form)", "Batch prediction (CSV upload)"], horizontal=True) # --------- Single prediction (manual form) --------- if mode == "Single prediction (form)": st.subheader("Single prediction") # Minimal & robust form: user enters main numeric features + selects a few one-hot options. # Because your training features are one-hot, we provide a simple way to set them. # Any feature not set will default to 0. # Detect numeric-ish columns (not perfect, but good for UI) numeric_cols = [c for c in feature_names if not any(c.startswith(prefix) for prefix in [ "BusinessTravel_", "Department_", "EducationField_", "Gender_", "JobRole_", "MaritalStatus_" ])] # Split numeric cols into two columns for nicer UI col_left, col_right = st.columns(2) form_values = {} with col_left: st.markdown("**Numeric / ordinal inputs**") # Provide a curated list of common HR numeric columns if present preferred_numeric = [ "Age", "DistanceFromHome", "Education", "EnvironmentSatisfaction", "HourlyRate", "JobInvolvement", "JobLevel", "JobSatisfaction", "MonthlyIncome", "MonthlyRate", "NumCompaniesWorked", "PercentSalaryHike", "PerformanceRating", "RelationshipSatisfaction", "StockOptionLevel", "TotalWorkingYears", "TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager", "OverTime", # might be 0/1 # engineered features (if you used these names) "tenure_ratio", "promotion_gap", "manager_stability", "income_per_level", "time_experience", "no_promotion", "income_experience", ] # Use preferred list if exists, else fallback to numeric_cols shown_numeric = [c for c in preferred_numeric if c in feature_names] or numeric_cols[:18] for c in shown_numeric: if c == "OverTime": form_values[c] = st.selectbox("OverTime (0=No, 1=Yes)", [0, 1], index=0) else: # default 0; user can adjust form_values[c] = st.number_input(c, value=0.0, step=1.0) with col_right: st.markdown("**Categorical one-hot selections**") st.caption("Select one option per group. Unselected groups remain 0 (baseline category).") # Map groups to their one-hot columns groups = { "BusinessTravel": [c for c in feature_names if c.startswith("BusinessTravel_")], "Department": [c for c in feature_names if c.startswith("Department_")], "EducationField": [c for c in feature_names if c.startswith("EducationField_")], "Gender": [c for c in feature_names if c.startswith("Gender_")], "JobRole": [c for c in feature_names if c.startswith("JobRole_")], "MaritalStatus": [c for c in feature_names if c.startswith("MaritalStatus_")], } # Initialize all one-hot group columns to 0 for gcols in groups.values(): for c in gcols: form_values[c] = 0 for gname, gcols in groups.items(): if not gcols: continue # Convert one-hot col name to label labels = ["(baseline / dropped category)"] + [c.split(f"{gname}_", 1)[1] for c in gcols] choice = st.selectbox(gname, labels, index=0) if choice != "(baseline / dropped category)": # Find matching one-hot column and set to 1 target_col = f"{gname}_{choice}" if target_col in form_values: form_values[target_col] = 1 # Ensure all missing features exist for c in feature_names: form_values.setdefault(c, 0) X_one = build_input_from_form(form_values) if st.button("Predict", type="primary"): pred, proba = predict_single(X_one) st.metric("Attrition probability (P=1)", f"{proba:.3f}") if pred == 1: st.error(f"Prediction: Attrition = 1 (Leave) | threshold={threshold:.2f}") else: st.success(f"Prediction: Attrition = 0 (Stay) | threshold={threshold:.2f}") with st.expander("Show input vector (aligned features)"): st.dataframe(X_one) # --------- Batch prediction (CSV upload) --------- else: st.subheader("Batch prediction (CSV upload)") st.write("Upload a CSV that already matches the training feature format (after preprocessing/one-hot).") st.caption("If your CSV is raw, preprocess it the same way as in your notebook before uploading.") uploaded = st.file_uploader("Upload CSV", type=["csv"]) if uploaded is not None: df_in = pd.read_csv(uploaded) # Drop target if user included it df_in = df_in.drop(columns=["Attrition"], errors="ignore") # Align columns to training feature set Xb = df_in.reindex(columns=feature_names, fill_value=0) probs = model.predict_proba(Xb)[:, 1] preds = (probs >= threshold).astype(int) out = df_in.copy() out["Attrition_proba"] = probs out["Attrition_pred"] = preds st.success(f"Predicted {len(out)} rows.") st.dataframe(out.head(20)) csv_bytes = out.to_csv(index=False).encode("utf-8") st.download_button("Download predictions CSV", data=csv_bytes, file_name="predictions.csv", mime="text/csv") st.divider() st.caption("Built with Streamlit • Model: XGBoost • Metric focus: ROC-AUC")