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milk_quality_prediction.py

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+"""
+================================================================
+  MILK QUALITY PREDICTION SYSTEM
+  Final Year Project — Machine Learning (Random Forest Classifier)
+  Author  : FYP Student
+  Supervisor: ML Expert
+  Dataset : Milk Quality Dataset (Kaggle) — milknew.csv
+================================================================
+"""
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import pandas as pd
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")          # non-interactive backend for file output
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import seaborn as sns
+import os
+
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (accuracy_score, confusion_matrix,
+                             classification_report, ConfusionMatrixDisplay)
+
+# ── output folder ──────────────────────────────────────────────────────────────
+OUT = "outputs"
+os.makedirs(OUT, exist_ok=True)
+
+PALETTE   = {"low": "#e74c3c", "medium": "#f39c12", "high": "#2ecc71"}
+LABEL_MAP = {0: "Low Quality", 1: "Medium Quality", 2: "High Quality"}
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 1.  DATA LOADING & PREPROCESSING
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n" + "="*65)
+print("  MILK QUALITY PREDICTION SYSTEM — FYP")
+print("="*65)
+
+print("\n[1/6]  Loading & Preprocessing Data …")
+
+df = pd.read_csv("milknew.csv")
+df.columns = df.columns.str.strip()          # strip any accidental spaces
+
+print(f"\n  ✔  Rows: {df.shape[0]}  |  Columns: {df.shape[1]}")
+print(f"\n  First 5 rows:\n{df.head().to_string(index=False)}")
+
+# --- missing values ---
+print(f"\n  Missing values per column:\n{df.isnull().sum().to_string()}")
+df.dropna(inplace=True)
+
+# --- encode target ---
+le = LabelEncoder()
+df["Grade_enc"] = le.fit_transform(df["Grade"])          # high=0, low=1, medium=2
+class_names = list(le.classes_)                          # ['high','low','medium']
+
+# nicer display order
+ORDER = ["low", "medium", "high"]
+
+# --- features & target ---
+FEATURES = ["pH", "Temprature", "Taste", "Odor", "Fat", "Turbidity", "Colour"]
+X = df[FEATURES].values
+y = df["Grade_enc"].values
+
+# --- scale ---
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# --- split ---
+X_train, X_test, y_train, y_test = train_test_split(
+    X_scaled, y, test_size=0.20, random_state=42, stratify=y)
+
+print(f"\n  Train samples : {len(X_train)}")
+print(f"  Test  samples : {len(X_test)}")
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 2.  EXPLORATORY DATA ANALYSIS
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n[2/6]  Performing EDA …")
+
+fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+fig.suptitle("Exploratory Data Analysis — Milk Quality Dataset",
+             fontsize=15, fontweight="bold", y=1.01)
+
+# ── 2a  Class distribution ──────────────────────────────────────────────────
+ax = axes[0, 0]
+counts = df["Grade"].value_counts().reindex(ORDER)
+bars = ax.bar(ORDER, counts.values,
+              color=[PALETTE[g] for g in ORDER], edgecolor="white", width=0.55)
+for b, v in zip(bars, counts.values):
+    ax.text(b.get_x() + b.get_width()/2, b.get_height() + 5,
+            str(v), ha="center", fontsize=11, fontweight="bold")
+ax.set_title("Class Distribution", fontweight="bold")
+ax.set_xlabel("Milk Quality Grade"); ax.set_ylabel("Count")
+ax.set_ylim(0, counts.max() * 1.15)
+ax.spines[["top","right"]].set_visible(False)
+
+# ── 2b  Correlation heatmap ─────────────────────────────────────────────────
+ax = axes[0, 1]
+corr = df[FEATURES + ["Grade_enc"]].corr()
+mask = np.triu(np.ones_like(corr, dtype=bool))
+sns.heatmap(corr, ax=ax, mask=mask, annot=True, fmt=".2f",
+            cmap="RdYlGn", center=0, linewidths=0.5,
+            cbar_kws={"shrink": 0.8})
+ax.set_title("Feature Correlation Heatmap", fontweight="bold")
+
+# ── 2c  pH distribution by grade ───────────────────────────────────────────
+ax = axes[1, 0]
+for grade in ORDER:
+    subset = df[df["Grade"] == grade]["pH"]
+    ax.hist(subset, bins=20, alpha=0.65,
+            color=PALETTE[grade], label=grade.capitalize(), edgecolor="white")
+ax.set_title("pH Distribution by Grade", fontweight="bold")
+ax.set_xlabel("pH"); ax.set_ylabel("Frequency")
+ax.legend()
+ax.spines[["top","right"]].set_visible(False)
+
+# ── 2d  Temperature distribution by grade ──────────────────────────────────
+ax = axes[1, 1]
+data_by_grade = [df[df["Grade"] == g]["Temprature"].values for g in ORDER]
+bp = ax.boxplot(data_by_grade, patch_artist=True, widths=0.55,
+                medianprops=dict(color="black", linewidth=2))
+for patch, grade in zip(bp["boxes"], ORDER):
+    patch.set_facecolor(PALETTE[grade])
+    patch.set_alpha(0.75)
+ax.set_xticklabels([g.capitalize() for g in ORDER])
+ax.set_title("Temperature Distribution by Grade", fontweight="bold")
+ax.set_xlabel("Grade"); ax.set_ylabel("Temperature (°C)")
+ax.spines[["top","right"]].set_visible(False)
+
+plt.tight_layout()
+eda_path = f"{OUT}/01_eda_overview.png"
+plt.savefig(eda_path, dpi=150, bbox_inches="tight")
+plt.close()
+print(f"  ✔  EDA chart saved → {eda_path}")
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 3.  MODEL DEVELOPMENT
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n[3/6]  Training Models …")
+
+# ── 3a  Hyperparameter tuning for Random Forest ─────────────────────────────
+param_grid = {
+    "n_estimators" : [50, 100, 200],
+    "max_depth"    : [None, 10, 20],
+    "min_samples_split": [2, 5],
+}
+gs = GridSearchCV(RandomForestClassifier(random_state=42),
+                  param_grid, cv=5, scoring="accuracy", n_jobs=-1)
+gs.fit(X_train, y_train)
+best_rf = gs.best_estimator_
+print(f"  ✔  Best RF params  : {gs.best_params_}")
+print(f"  ✔  Best CV accuracy: {gs.best_score_:.4f}")
+
+# ── 3b  Comparison models ────────────────────────────────────────────────────
+dt = DecisionTreeClassifier(max_depth=10, random_state=42)
+lr = LogisticRegression(max_iter=1000, random_state=42)
+dt.fit(X_train, y_train); lr.fit(X_train, y_train)
+
+models = {
+    "Random Forest"     : best_rf,
+    "Decision Tree"     : dt,
+    "Logistic Regression": lr,
+}
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 4.  MODEL EVALUATION
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n[4/6]  Evaluating Models …")
+
+results = {}
+for name, mdl in models.items():
+    y_pred = mdl.predict(X_test)
+    acc    = accuracy_score(y_test, y_pred)
+    cv_sc  = cross_val_score(mdl, X_scaled, y, cv=5, scoring="accuracy")
+    results[name] = {
+        "accuracy"  : acc,
+        "cv_mean"   : cv_sc.mean(),
+        "cv_std"    : cv_sc.std(),
+        "y_pred"    : y_pred,
+        "cm"        : confusion_matrix(y_test, y_pred),
+        "report"    : classification_report(y_test, y_pred,
+                          target_names=class_names, output_dict=True),
+    }
+    print(f"\n  ── {name} ──")
+    print(f"     Test Accuracy : {acc:.4f}  |  CV: {cv_sc.mean():.4f} ± {cv_sc.std():.4f}")
+    print(classification_report(y_test, y_pred, target_names=class_names))
+
+rf_res = results["Random Forest"]
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 5.  VISUALISATIONS
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n[5/6]  Generating Visualisations …")
+
+# ── 5a  Accuracy comparison ──���──────────────────────────────────────────────
+fig, ax = plt.subplots(figsize=(9, 5))
+names  = list(results.keys())
+accs   = [results[n]["accuracy"] for n in names]
+cv_m   = [results[n]["cv_mean"]  for n in names]
+cv_s   = [results[n]["cv_std"]   for n in names]
+x      = np.arange(len(names))
+w      = 0.35
+colors = ["#3498db", "#e67e22", "#9b59b6"]
+
+bars1 = ax.bar(x - w/2, accs,  w, label="Test Accuracy",
+               color=colors, edgecolor="white", alpha=0.9)
+bars2 = ax.bar(x + w/2, cv_m,  w, yerr=cv_s, label="CV Accuracy (mean ± std)",
+               color=colors, edgecolor="white", alpha=0.55, capsize=5)
+
+for bar in list(bars1) + list(bars2):
+    h = bar.get_height()
+    ax.text(bar.get_x() + bar.get_width()/2, h + 0.005,
+            f"{h:.3f}", ha="center", va="bottom", fontsize=8.5, fontweight="bold")
+
+ax.set_xticks(x); ax.set_xticklabels(names, fontsize=11)
+ax.set_ylim(0.6, 1.05)
+ax.set_ylabel("Accuracy Score", fontsize=12)
+ax.set_title("Model Accuracy Comparison\n(Test vs 5-Fold Cross-Validation)",
+             fontsize=13, fontweight="bold")
+ax.legend(fontsize=10)
+ax.spines[["top","right"]].set_visible(False)
+ax.axhline(1.0, color="gray", linestyle="--", linewidth=0.7)
+plt.tight_layout()
+plt.savefig(f"{OUT}/02_accuracy_comparison.png", dpi=150, bbox_inches="tight")
+plt.close()
+
+# ── 5b  Confusion matrix — Random Forest ─────────────────────────────────
+fig, ax = plt.subplots(figsize=(7, 6))
+cm_disp = ConfusionMatrixDisplay(
+    confusion_matrix=rf_res["cm"],
+    display_labels=[c.capitalize() for c in class_names])
+cm_disp.plot(ax=ax, colorbar=True, cmap="Blues")
+ax.set_title("Confusion Matrix — Random Forest", fontsize=13, fontweight="bold")
+plt.tight_layout()
+plt.savefig(f"{OUT}/03_confusion_matrix_rf.png", dpi=150, bbox_inches="tight")
+plt.close()
+
+# ── 5c  Feature importance ──────────────────────────────────────────────────
+importances = best_rf.feature_importances_
+idx = np.argsort(importances)[::-1]
+feat_sorted  = [FEATURES[i] for i in idx]
+imp_sorted   = importances[idx]
+
+fig, ax = plt.subplots(figsize=(9, 5))
+bar_colors = plt.cm.RdYlGn(np.linspace(0.25, 0.85, len(feat_sorted)))[::-1]
+bars = ax.barh(feat_sorted[::-1], imp_sorted[::-1],
+               color=bar_colors[::-1], edgecolor="white")
+for bar, val in zip(bars, imp_sorted[::-1]):
+    ax.text(bar.get_width() + 0.003, bar.get_y() + bar.get_height()/2,
+            f"{val:.3f}", va="center", fontsize=10, fontweight="bold")
+ax.set_xlabel("Feature Importance (Gini)", fontsize=12)
+ax.set_title("Feature Importance — Random Forest Classifier",
+             fontsize=13, fontweight="bold")
+ax.spines[["top","right"]].set_visible(False)
+ax.set_xlim(0, imp_sorted.max() * 1.15)
+plt.tight_layout()
+plt.savefig(f"{OUT}/04_feature_importance.png", dpi=150, bbox_inches="tight")
+plt.close()
+
+# ── 5d  Per-class F1-score comparison across models ─────────────────────────
+fig, ax = plt.subplots(figsize=(10, 5))
+model_names = list(results.keys())
+x = np.arange(len(class_names))
+width = 0.25
+hatch_list = ["", "//", ".."]
+
+for i, (mname, hatch) in enumerate(zip(model_names, hatch_list)):
+    f1_vals = [results[mname]["report"][c]["f1-score"] for c in class_names]
+    offset = (i - 1) * width
+    bars = ax.bar(x + offset, f1_vals, width, label=mname,
+                  alpha=0.85, edgecolor="white", hatch=hatch)
+
+ax.set_xticks(x)
+ax.set_xticklabels([c.capitalize() for c in class_names], fontsize=11)
+ax.set_ylabel("F1-Score", fontsize=12)
+ax.set_ylim(0, 1.1)
+ax.set_title("Per-Class F1-Score Comparison Across Models",
+             fontsize=13, fontweight="bold")
+ax.legend(fontsize=10)
+ax.axhline(1.0, color="gray", linestyle="--", linewidth=0.7)
+ax.spines[["top","right"]].set_visible(False)
+plt.tight_layout()
+plt.savefig(f"{OUT}/05_f1_comparison.png", dpi=150, bbox_inches="tight")
+plt.close()
+
+print("  ✔  All charts saved to outputs/")
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 6.  PREDICTION SYSTEM  (demo with 5 sample inputs)
+# ══════════════════════════════════════════════════════════════════════════════
+print("\n[6/6]  Real-Time Prediction System Demo …")
+
+QUALITY_LABELS = {"high": "High Quality 🟢", "low": "Low Quality 🔴", "medium": "Medium Quality 🟡"}
+
+def predict_milk_quality(ph, temperature, taste, odor, fat, turbidity, colour):
+    """
+    Predict milk quality from physicochemical properties.
+
+    Parameters
+    ----------
+    ph          : float  (typically 3–9.5)
+    temperature : float  (°C)
+    taste       : int    (1 = good, 0 = bad)
+    odor        : int    (1 = good, 0 = bad)
+    fat         : int    (1 = present, 0 = absent)
+    turbidity   : int    (1 = turbid, 0 = clear)
+    colour      : int    (240–255 typical range)
+
+    Returns
+    -------
+    dict with grade, confidence, and per-class probabilities
+    """
+    raw = np.array([[ph, temperature, taste, odor, fat, turbidity, colour]])
+    scaled = scaler.transform(raw)
+    grade_enc  = best_rf.predict(scaled)[0]
+    proba      = best_rf.predict_proba(scaled)[0]
+    grade_str  = le.inverse_transform([grade_enc])[0]          # 'high'/'low'/'medium'
+    confidence = proba[grade_enc] * 100
+    prob_dict  = {le.inverse_transform([i])[0]: round(p*100, 2)
+                  for i, p in enumerate(proba)}
+    return {
+        "grade"      : QUALITY_LABELS[grade_str],
+        "raw_grade"  : grade_str,
+        "confidence" : round(confidence, 2),
+        "probabilities": prob_dict,
+    }
+
+# ── sample predictions ───────────────────────────────────────────────────────
+samples = [
+    # (pH,  Temp, Taste, Odor, Fat, Turbidity, Colour)  — expected grade
+    (6.6,  35,    1,     0,    1,   0,          254),    # likely high
+    (8.5,  70,    1,     1,    1,   1,          246),    # likely low
+    (6.7,  37,    0,     1,    0,   1,          253),    # likely medium
+    (3.5,  34,    1,     1,    0,   1,          255),    # likely low
+    (6.8,  40,    1,     1,    1,   0,          250),    # likely high
+]
+
+print("\n  ┌─────────────────────────────────────────────────────────────────┐")
+print("  │              MILK QUALITY PREDICTION RESULTS                    │")
+print("  ├────┬───────────────────────┬───────────┬────────────────────────┤")
+print("  │ #  │  Grade                │ Confidence│ Probabilities (%)      │")
+print("  ├────┼───────────────────────┼───────────┼────────────────────────┤")
+
+for i, args in enumerate(samples, 1):
+    res = predict_milk_quality(*args)
+    prob_str = "  ".join([f"{k[:3].upper()}:{v:.1f}" for k, v in sorted(res["probabilities"].items())])
+    print(f"  │ {i:<2} │ {res['grade']:<21} │  {res['confidence']:>6.2f}%  │ {prob_str:<22} │")
+
+print("  └────┴───────────────────────┴───────────┴────────────────────────┘")
+
+# ── 6b  Prediction dashboard visual ─────────────────────────────────────────
+fig, axes = plt.subplots(1, len(samples), figsize=(14, 4))
+fig.suptitle("Prediction Dashboard — Sample Milk Specimens",
+             fontsize=13, fontweight="bold")
+
+grade_colors = {"high": "#2ecc71", "medium": "#f39c12", "low": "#e74c3c"}
+
+for ax, args in zip(axes, samples):
+    res = predict_milk_quality(*args)
+    probs = res["probabilities"]
+    grades = ["high", "medium", "low"]
+    vals   = [probs[g] for g in grades]
+    colors = [grade_colors[g] for g in grades]
+    bars = ax.bar(grades, vals, color=colors, edgecolor="white", alpha=0.85)
+    ax.set_ylim(0, 110)
+    ax.set_title(f"pH={args[0]}, T={args[1]}°C", fontsize=9)
+    ax.set_ylabel("Probability (%)", fontsize=8)
+    ax.tick_params(axis="x", labelsize=8)
+    ax.spines[["top","right"]].set_visible(False)
+    # annotate predicted class
+    best_g = res["raw_grade"]
+    ax.text(0.5, 0.95, f"→ {best_g.upper()}",
+            transform=ax.transAxes, ha="center", va="top",
+            fontsize=10, fontweight="bold", color=grade_colors[best_g],
+            bbox=dict(boxstyle="round,pad=0.3", fc="white", ec=grade_colors[best_g], lw=1.5))
+
+legend_patches = [mpatches.Patch(color=grade_colors[g], label=g.capitalize()) for g in grades]
+fig.legend(handles=legend_patches, loc="lower right", fontsize=9, ncol=3)
+plt.tight_layout()
+plt.savefig(f"{OUT}/06_prediction_dashboard.png", dpi=150, bbox_inches="tight")
+plt.close()
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 7.  SUMMARY REPORT
+# ══════════════════════════════════════════════════════════════════════════════
+rf_acc = rf_res["accuracy"]
+rf_cv  = rf_res["cv_mean"]
+rf_std = rf_res["cv_std"]
+top_feat = feat_sorted[0]
+
+report_text = f"""
+================================================================
+  MILK QUALITY PREDICTION SYSTEM — FINAL REPORT
+================================================================
+Dataset      : milknew.csv  ({df.shape[0]} samples, {df.shape[1]-1} features)
+Target       : Grade  →  Low / Medium / High
+Train/Test   : 80% / 20%  (stratified split)
+
+── Model Performance Summary ────────────────────────────────────
+Model                  Test Acc   CV Acc (5-fold)
+─────────────────────────────────────────────────
+Random Forest          {results['Random Forest']['accuracy']:.4f}     {results['Random Forest']['cv_mean']:.4f} ± {results['Random Forest']['cv_std']:.4f}
+Decision Tree          {results['Decision Tree']['accuracy']:.4f}     {results['Decision Tree']['cv_mean']:.4f} ± {results['Decision Tree']['cv_std']:.4f}
+Logistic Regression    {results['Logistic Regression']['accuracy']:.4f}     {results['Logistic Regression']['cv_mean']:.4f} ± {results['Logistic Regression']['cv_std']:.4f}
+
+── Best Model: Random Forest ────────────────────────────────────
+Best Hyperparameters : {gs.best_params_}
+Test Accuracy        : {rf_acc:.4f}
+Cross-Val Accuracy   : {rf_cv:.4f} ± {rf_std:.4f}
+Top Feature          : {top_feat}
+
+── Feature Importances (Random Forest) ─────────────────────────
+{chr(10).join([f"  {f:<12} : {importances[FEATURES.index(f)]:.4f}" for f in feat_sorted])}
+
+── Classification Report (Random Forest) ────────────────────────
+{classification_report(y_test, rf_res['y_pred'], target_names=class_names)}
+
+── Output Files ─────────────────────────────────────────────────
+  01_eda_overview.png
+  02_accuracy_comparison.png
+  03_confusion_matrix_rf.png
+  04_feature_importance.png
+  05_f1_comparison.png
+  06_prediction_dashboard.png
+  summary_report.txt
+================================================================
+"""
+
+report_path = f"{OUT}/summary_report.txt"
+with open(report_path, "w") as f:
+    f.write(report_text)
+
+print(report_text)
+print("  ✔  Summary report saved →", report_path)
+print("\n  ══════════════════════════════════════")
+print("  FYP pipeline completed successfully! 🎓")
+print("  ══════════════════════════════════════\n")

milknew.csv

@@ -0,0 +1,1060 @@
+pH,Temprature,Taste,Odor,Fat ,Turbidity,Colour,Grade
+6.6,35,1,0,1,0,254,high
+6.6,36,0,1,0,1,253,high
+8.5,70,1,1,1,1,246,low
+9.5,34,1,1,0,1,255,low
+6.6,37,0,0,0,0,255,medium
+6.6,37,1,1,1,1,255,high
+5.5,45,1,0,1,1,250,low
+4.5,60,0,1,1,1,250,low
+8.1,66,1,0,1,1,255,low
+6.7,45,1,1,0,0,247,medium
+6.7,45,1,1,1,0,245,medium
+5.6,50,0,1,1,1,255,low
+8.6,55,0,1,1,1,255,low
+7.4,90,1,0,1,1,255,low
+6.8,45,0,1,1,1,255,high
+6.5,38,1,0,0,0,255,medium
+4.7,38,1,0,1,0,255,low
+3,40,1,1,1,1,255,low
+9,43,1,0,1,1,250,low
+6.8,40,1,0,1,0,245,medium
+6.6,45,0,1,1,1,250,high
+6.5,36,0,0,1,0,255,medium
+4.5,38,0,1,1,1,255,low
+6.6,45,1,1,1,1,245,high
+6.8,35,1,0,1,0,246,medium
+6.5,36,0,1,1,0,253,high
+8.5,70,0,0,0,0,246,low
+6.6,34,0,0,0,1,240,medium
+6.5,37,0,0,0,0,245,medium
+6.6,37,1,0,1,0,255,high
+6.4,45,0,1,0,0,240,medium
+6.5,40,0,1,0,1,250,low
+6.8,42,1,1,1,1,255,high
+6.7,41,1,0,0,0,247,medium
+6.7,50,1,1,1,0,245,low
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summary_report.txt

@@ -0,0 +1,51 @@
+
+================================================================
+  MILK QUALITY PREDICTION SYSTEM — FINAL REPORT
+================================================================
+Dataset      : milknew.csv  (1059 samples, 8 features)
+Target       : Grade  →  Low / Medium / High
+Train/Test   : 80% / 20%  (stratified split)
+
+── Model Performance Summary ────────────────────────────────────
+Model                  Test Acc   CV Acc (5-fold)
+─────────────────────────────────────────────────
+Random Forest          0.9953     0.9962 ± 0.0046
+Decision Tree          0.9953     0.9934 ± 0.0082
+Logistic Regression    0.8302     0.8518 ± 0.0046
+
+── Best Model: Random Forest ────────────────────────────────────
+Best Hyperparameters : {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 50}
+Test Accuracy        : 0.9953
+Cross-Val Accuracy   : 0.9962 ± 0.0046
+Top Feature          : pH
+
+── Feature Importances (Random Forest) ─────────────────────────
+  pH           : 0.4024
+  Temprature   : 0.2332
+  Fat          : 0.1054
+  Turbidity    : 0.0849
+  Odor         : 0.0740
+  Colour       : 0.0571
+  Taste        : 0.0429
+
+── Classification Report (Random Forest) ────────────────────────
+              precision    recall  f1-score   support
+
+        high       1.00      0.98      0.99        51
+         low       0.99      1.00      0.99        86
+      medium       1.00      1.00      1.00        75
+
+    accuracy                           1.00       212
+   macro avg       1.00      0.99      0.99       212
+weighted avg       1.00      1.00      1.00       212
+
+
+── Output Files ─────────────────────────────────────────────────
+  01_eda_overview.png
+  02_accuracy_comparison.png
+  03_confusion_matrix_rf.png
+  04_feature_importance.png
+  05_f1_comparison.png
+  06_prediction_dashboard.png
+  summary_report.txt
+================================================================