diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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+.venv/
+__pycache__/
diff --git a/gpc_encoder.py b/gpc_encoder.py
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+# Import
+import numpy as np
+import pandas as pd
+
+# Define functions
+def make_random_projection_matrix(K: int, d: int, rng: np.random.Generator) -> np.ndarray:
+    """
+    Create a random projection matrix R of shape (K, d) to project
+    K-dimensional vectors into d-dimensional space.
+
+    The matrix entries are sampled from a Gaussian distribution:
+        N(0, 1) / sqrt(d)
+
+    Parameters
+    ----------
+    K : int
+        Original dimensionality (number of categories).
+    d : int
+        Target embedding dimension.
+    rng : np.random.Generator
+        NumPy random number generator for reproducibility.
+
+    Returns
+    -------
+    np.ndarray
+        Random projection matrix of shape (K, d).
+    """
+    R = rng.normal(0.0, 1.0, size=(K, d)) / np.sqrt(d)
+    return R
+
+# Define GPC Encoder (Geoetry Preserving Category Encoder)
+def gpc_encoder(X_train: pd.DataFrame, X_test: pd.DataFrame, cat_cols: list[str], d: int = 16, r: float = 1.0, seed: int = 42,) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Geometry Preserving Category Encoder (GPCE).
+
+    For each categorical column:
+    - Categories are mapped (based on TRAIN data only) to vectors
+      lying on a hypersphere of radius r.
+    - Category geometry is preserved by:
+        1) Centering one-hot vectors
+        2) Scaling to fixed norm
+        3) Applying random projection
+        4) Re-normalizing after projection
+    - The same mapping is applied to both train and test sets.
+
+    The resulting d-dimensional vectors are expanded into d numerical
+    columns and concatenated with the original numerical features.
+
+    Parameters
+    ----------
+    X_train : pd.DataFrame
+        Training feature matrix.
+    X_test : pd.DataFrame
+        Test feature matrix.
+    cat_cols : list[str]
+        List of categorical column names to encode.
+    d : int, default=16
+        Target embedding dimension for each categorical feature.
+    r : float, default=1.0
+        Radius of the hypersphere on which category vectors lie.
+    seed : int, default=42
+        Random seed for reproducibility.
+
+    Returns
+    -------
+    (pd.DataFrame, pd.DataFrame)
+        Encoded training and test feature matrices.
+    """
+
+    rng = np.random.default_rng(seed)
+
+    # Separate numerical features
+    Xtr_num = X_train.drop(columns=cat_cols)
+    Xte_num = X_test.drop(columns=cat_cols)
+
+    tr_blocks = [Xtr_num]
+    te_blocks = [Xte_num]
+
+    for col in cat_cols:
+        # Treat missing values as a separate category to ensure consistency
+        tr = X_train[col].astype("object")
+        te = X_test[col].astype("object")
+
+        # 1. Determine category set from TRAIN data only
+        cats = pd.unique(tr)
+        K = len(cats)
+        if K < 2:
+            # If there is only one category, no discriminative information exists.
+            # Assign a constant vector on the hypersphere.
+            const_vec = np.zeros(d, dtype=float)
+            const_vec[0] = r
+            mapping = {cats[0]: const_vec}
+            fallback = const_vec
+        else:
+            # 2. Construct standard basis vectors e_i (identity matrix)
+            I = np.eye(K, dtype=float)
+
+            # 3. Compute centroid (1/K) * 1 vector
+            ones = np.ones((K,), dtype=float) / K
+
+            # 4. Center the basis vectors: u_i = e_i - (1/K) * 1
+            U = I - ones[None, :] # shape (K, K)
+
+            # 5. Scale vectors so that ||v_i|| = r
+            #    ||u_i|| = sqrt((K - 1) / K)
+            #    v_i = r * sqrt(K/(K-1)) * u_i
+            scale = r * np.sqrt(K / (K - 1))
+            V = scale * U # shape (K, K)
+
+            # 6. Random projection from K -> d dimensions
+            #    (each column uses an independent projection matrix)
+            col_seed = int(rng.integers(0, 2**31 - 1))
+            R = make_random_projection_matrix(K=K, d=d, rng=np.random.default_rng(col_seed))
+            Z = V @ R # shape (K, d)
+
+            # 7. Re-normalize projected vectors to radius r
+            norms = np.linalg.norm(Z, axis=1, keepdims=True)
+            Z = (Z / norms) * r
+
+            # 8. Build category -> vector mapping
+            mapping = {cat: Z[i] for i, cat in enumerate(cats)}
+
+            # 9. Fallback vector for unseen categories (mean direction)
+            mean_vec = Z.mean(axis=0)
+            mv = np.linalg.norm(mean_vec)
+
+            if mv < 1e-12:
+                # Degenerate case: generate random direction
+                tmp = np.random.default_rng(col_seed).normal(size=d)
+                mean_vec = (tmp / np.linalg.norm(tmp)) * r
+            else:
+                mean_vec = (mean_vec / mv) * r
+            fallback = mean_vec
+
+        # Transform a categorical series into a matrix of shape (N, d)
+        def to_matrix(series: pd.Series) -> np.ndarray:
+            out = np.zeros((len(series), d), dtype=float)
+            for i, v in enumerate(series):
+                out[i] = mapping.get(v, fallback)
+            return out
+
+        # Encode train and test columns
+        Ztr = to_matrix(tr)
+        Zte = to_matrix(te)
+
+        # Generate column names for expanded vectors
+        new_cols = [f"{col}__gpce_{j}" for j in range(d)]
+
+        tr_blocks.append(pd.DataFrame(Ztr, columns=new_cols, index=X_train.index))
+        te_blocks.append(pd.DataFrame(Zte, columns=new_cols, index=X_test.index))
+
+    # Concatenate numerical and encoded categorical features
+    Xtr_out = pd.concat(tr_blocks, axis=1)
+    Xte_out = pd.concat(te_blocks, axis=1)
+
+    return Xtr_out, Xte_out
diff --git a/main.py b/main.py
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+++ b/main.py
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+# Import standard libraries
+import os
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+# Import model selection
+from sklearn.model_selection import train_test_split
+
+# Import preprocessing and pipelines
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+
+# Import classifiers
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.neural_network import MLPClassifier
+
+# Import metrics
+from sklearn.metrics import accuracy_score
+
+# Import encoder
+from gpc_encoder import gpc_encoder
+from sklearn.preprocessing import LabelEncoder
+from category_encoders import TargetEncoder, OneHotEncoder
+
+# CONFIG
+TARGET_COL = '' # Target feature's name
+CSV_FILE_NAME = '' # CSV file's name
+DATA_LEN_LIMIT = 10000 # Limits the number of rows in the data
+RANDOM_SEED = 42
+MODELS = {"LogReg": Pipeline([("scaler", StandardScaler()), ("clf", LogisticRegression(max_iter=2000))]),
+          "DecisionTree": DecisionTreeClassifier(random_state=42),
+          "RandomForest": RandomForestClassifier(n_estimators=300, random_state=42, n_jobs=-1),
+          "KNN": Pipeline([("scaler", StandardScaler()), ("clf", KNeighborsClassifier(n_neighbors=15))]),
+          "SVM(RBF)": Pipeline([("scaler", StandardScaler()), ("clf", SVC(kernel="rbf"))]),
+          "MLP": Pipeline([("scaler", StandardScaler()), ("clf", MLPClassifier(hidden_layer_sizes=(64, 32), max_iter=500, random_state=42))])}
+ENCODERS= ["GPCE", "Label", "OneHot", "Target"]
+D = 16 # Targeted dimension
+R = 1.0 # Distance
+TEST_SIZE = 0.2 # Test data size
+
+# Load data
+df = pd.read_csv(f"./datasets/{CSV_FILE_NAME}")
+
+if DATA_LEN_LIMIT:
+    df = df[:DATA_LEN_LIMIT]
+
+tar_dtype = df[TARGET_COL].dtype
+if tar_dtype == 'object' or tar_dtype == 'category':
+    df[TARGET_COL] = LabelEncoder().fit_transform(df[TARGET_COL])
+
+cat_cols = df.drop(TARGET_COL, axis=1).select_dtypes(include=['object', 'category']).columns.tolist()
+
+df = df.dropna().copy()
+
+# Split X and y
+X = df.drop(columns=[TARGET_COL])
+y = df[TARGET_COL].to_numpy()
+
+# Split train and test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=RANDOM_SEED, stratify=y if len(np.unique(y)) > 1 else None)
+
+# Encode categorical features
+def get_encoded_data(method, X_train, X_test, y_train, cat_cols):
+    if method == "GPCE":
+        X_train_enc, X_test_enc = gpc_encoder(X_train=X_train, X_test=X_test, cat_cols=cat_cols, d=D, r=R, seed=RANDOM_SEED)
+        return X_train_enc, X_test_enc
+
+    elif method == "Label":
+        X_train_enc, X_test_enc = X_train.copy(), X_test.copy()
+        for col in cat_cols:
+            le = LabelEncoder()
+            full_data = pd.concat([X_train_enc[col], X_test_enc[col]]).astype(str)
+            le.fit(full_data)
+            X_train_enc[col] = le.transform(X_train_enc[col].astype(str))
+            X_test_enc[col] = le.transform(X_test_enc[col].astype(str))
+        return X_train_enc, X_test_enc
+
+    elif method == "OneHot":
+        ohe = OneHotEncoder(cols=cat_cols, use_cat_names=True, handle_unknown='value')
+        X_train_enc = ohe.fit_transform(X_train, y_train)
+        X_test_enc = ohe.transform(X_test)
+        return X_train_enc, X_test_enc
+
+    elif method == "Target":
+        te = TargetEncoder(cols=cat_cols)
+        X_train_enc = te.fit_transform(X_train, y_train)
+        X_test_enc = te.transform(X_test)
+        return X_train_enc, X_test_enc
+
+    return X_train, X_test
+
+results = {}
+for encoder in tqdm(
+    ENCODERS,
+    desc="Encoding method",
+    position=0
+):
+    results[encoder] = {}
+    X_train_enc, X_test_enc = get_encoded_data(method=encoder, X_train=X_train, X_test=X_test, y_train=y_train, cat_cols=cat_cols)
+
+    for name, model in tqdm(
+        MODELS.items(),
+        desc=f"Models ({encoder})",
+        position=1,
+        leave=False
+    ):
+        # Train the model
+        model.fit(X_train_enc, y_train)
+        # Evaluate the model
+        pred = model.predict(X_test_enc)
+        accuracy = accuracy_score(y_test, pred)
+        # Save the result
+        results[encoder][name] = float(accuracy)
+
+results_df = pd.DataFrame(results).T
+print("========================== Accuracy Result =========================")
+print(results_df)
+
+# Visualize
+models = results_df.columns.tolist()
+encoders = results_df.index.tolist()
+
+n_models = len(models)
+n_encoders = len(encoders)
+
+x = np.arange(n_models)
+bar_width = 0.8 / n_encoders
+
+gray_levels = np.linspace(0.85, 0.25, len(encoders))
+
+min_y = max(results_df.min().min() - 0.1, 0)
+max_y = min(results_df.max().max() + 0.1, 1)
+
+plt.figure(figsize=(12, 5))
+
+for i, (enc, gray) in enumerate(zip(encoders, gray_levels)):
+    offsets = x - 0.4 + (i + 0.5) * bar_width
+    plt.bar(
+        offsets,
+        results_df.loc[enc].values,
+        width=bar_width,
+        color=str(gray_levels[i]),
+        edgecolor="black",
+        label=enc
+    )
+
+plt.xticks(x, models, rotation=0)
+plt.xlabel("Model")
+plt.ylabel("Accuracy")
+plt.title(f"Comparison of Model Accuracy Across Encoders ({CSV_FILE_NAME})")
+plt.ylim(min_y, max_y)
+plt.legend(title="Encoder", ncol=min(n_encoders, 4))
+plt.grid(axis="y", alpha=0.3)
+plt.tight_layout()
+
+output_dir = "results"
+file_name = f"{CSV_FILE_NAME.replace('.csv', '')}.png"
+full_path = os.path.join(output_dir, file_name)
+os.makedirs(output_dir, exist_ok=True)
+plt.savefig(full_path, dpi=500)
+
+plt.show()
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