diff --git a/app/clients/service/MLmodels/linear_model_subset.pkl b/app/clients/service/MLmodels/linear_model_subset.pkl
new file mode 100644
index 00000000..353e3eb6
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diff --git a/app/clients/service/model.pkl b/app/clients/service/MLmodels/model.pkl
similarity index 100%
rename from app/clients/service/model.pkl
rename to app/clients/service/MLmodels/model.pkl
diff --git a/app/clients/service/MLmodels/ridge_model.pkl b/app/clients/service/MLmodels/ridge_model.pkl
new file mode 100644
index 00000000..0a829cd7
Binary files /dev/null and b/app/clients/service/MLmodels/ridge_model.pkl differ
diff --git a/app/clients/service/MLmodels/xgboost_model.pkl b/app/clients/service/MLmodels/xgboost_model.pkl
new file mode 100644
index 00000000..6bc4004c
Binary files /dev/null and b/app/clients/service/MLmodels/xgboost_model.pkl differ
diff --git a/app/clients/service/analyze_data/analyze_features.py b/app/clients/service/analyze_data/analyze_features.py
new file mode 100644
index 00000000..e1093db9
--- /dev/null
+++ b/app/clients/service/analyze_data/analyze_features.py
@@ -0,0 +1,33 @@
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def analyze_features(csv_path, target_col="success_rate", threshold=0.1):
+    # Load dataset
+    df = pd.read_csv(csv_path)
+
+    # Plot correlation heatmap for all features
+    corr = df.corr()
+    plt.figure(figsize=(14, 12))
+    sns.heatmap(corr, annot=True, cmap="coolwarm")
+    plt.title("Correlation Heatmap")
+    plt.show()
+
+    # Select features with correlation above threshold w.r.t target
+    target_corr = corr[target_col].drop(target_col)  # drop self-correlation
+    recommended_features = target_corr[abs(target_corr) >= threshold].index.tolist()
+
+    print("Recommended features (|correlation with '{}'| >= {:.2f}):".format(target_col, threshold))
+    print(recommended_features)
+    
+    return recommended_features
+
+
+if __name__ == "__main__":
+    # Dynamically build relative path to your dataset
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    csv_path = os.path.join(BASE_DIR, '../../clients/service/data_commontool_synthetic.csv')
+
+    # Call feature analysis
+    selected_features = analyze_features(csv_path)
diff --git a/app/clients/service/data_commontool_synthetic.csv b/app/clients/service/data_commontool_synthetic.csv
new file mode 100644
index 00000000..2f734334
--- /dev/null
+++ b/app/clients/service/data_commontool_synthetic.csv
@@ -0,0 +1,7001 @@
+age,gender,work_experience,canada_workex,dep_num,canada_born,citizen_status,level_of_schooling,fluent_english,reading_english_scale,speaking_english_scale,writing_english_scale,numeracy_scale,computer_scale,transportation_bool,caregiver_bool,housing,income_source,felony_bool,attending_school,currently_employed,substance_use,time_unemployed,need_mental_health_support_bool,employment_assistance,life_stabilization,retention_services,specialized_services,employment_related_financial_supports,employer_financial_supports,enhanced_referrals,success_rate
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diff --git a/app/clients/service/data_commontool_synthetic_testdata.csv b/app/clients/service/data_commontool_synthetic_testdata.csv
new file mode 100644
index 00000000..aff9e2fb
--- /dev/null
+++ b/app/clients/service/data_commontool_synthetic_testdata.csv
@@ -0,0 +1,4001 @@
+age,gender,work_experience,canada_workex,dep_num,canada_born,citizen_status,level_of_schooling,fluent_english,reading_english_scale,speaking_english_scale,writing_english_scale,numeracy_scale,computer_scale,transportation_bool,caregiver_bool,housing,income_source,felony_bool,attending_school,currently_employed,substance_use,time_unemployed,need_mental_health_support_bool,employment_assistance,life_stabilization,retention_services,specialized_services,employment_related_financial_supports,employer_financial_supports,enhanced_referrals,success_rate
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diff --git a/app/clients/service/final_MLmodels/final_linear_model_subset.pkl b/app/clients/service/final_MLmodels/final_linear_model_subset.pkl
new file mode 100644
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diff --git a/app/clients/service/final_MLmodels/final_ridge_model.pkl b/app/clients/service/final_MLmodels/final_ridge_model.pkl
new file mode 100644
index 00000000..5b94e384
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diff --git a/app/clients/service/final_MLmodels/final_xgboost_gridcv_model.pkl b/app/clients/service/final_MLmodels/final_xgboost_gridcv_model.pkl
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diff --git a/app/clients/service/initial_model_trainers/XGBoost_model.py b/app/clients/service/initial_model_trainers/XGBoost_model.py
new file mode 100644
index 00000000..47424361
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/XGBoost_model.py
@@ -0,0 +1,89 @@
+import os
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from xgboost import XGBRegressor
+
+# Define feature set
+FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+# Set relative paths
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(BASE_DIR, '../data_commontool_synthetic.csv')
+MODEL_DIR = os.path.join(BASE_DIR, '../MLmodels/')   
+os.makedirs(MODEL_DIR, exist_ok=True)  # Auto-create directory if it doesn't exist
+
+def load_data(selected_features):
+    """Load dataset and return features and targets"""
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def train_xgboost(X_train, y_train):
+    """Train XGBoost Regressor"""
+    model = XGBRegressor(
+        n_estimators=100,
+        max_depth=5,
+        learning_rate=0.1,
+        objective='reg:squarederror',
+        random_state=42
+    )
+    model.fit(X_train, y_train)
+    return model
+
+def evaluate_model(model, X_test, y_test):
+    """Evaluate model on the hold-out test set"""
+    predictions = model.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+
+def cross_validate(model, X, y):
+    """Perform 5-fold cross-validation"""
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    """Save trained model as a pickle file"""
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ XGBoost model saved at {model_path}")
+
+def main():
+    # Load data
+    X, y = load_data(FEATURES)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    # Train model
+    model = train_xgboost(X_train, y_train)
+    
+    # Evaluate on hold-out test set
+    evaluate_model(model, X_test, y_test)
+    
+    # Cross-validation
+    cross_validate(model, X, y)
+    
+    # Save model
+    save_model(model, "xgboost_model.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/initial_model_trainers/get_synthetic_data.py b/app/clients/service/initial_model_trainers/get_synthetic_data.py
new file mode 100644
index 00000000..9e9091b8
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/get_synthetic_data.py
@@ -0,0 +1,90 @@
+import pandas as pd
+import numpy as np
+import os
+
+num_samples = 7000  # Total number of samples to generate
+
+# Age distribution: Normal distribution centered around 30, capped between 18 and 60
+age = np.random.normal(loc=30, scale=8, size=num_samples).astype(int)
+age = np.clip(age, 18, 60)
+
+# Work experience based on age (years since 18 minus some gap years)
+work_experience = (age - 18) - np.random.randint(0, 4, num_samples)
+work_experience = np.clip(work_experience, 0, 42)
+
+# Canadian work experience is a fraction of total work experience
+canada_workex = (work_experience * np.random.uniform(0.3, 0.9, num_samples)).astype(int)
+
+# Education level: weighted choice simulating real-world distribution
+level_of_schooling = np.random.choice([6, 8, 10, 12, 14], size=num_samples, p=[0.1, 0.25, 0.3, 0.25, 0.1])
+
+# English proficiency: fluent_english impacts reading/speaking/writing scores
+fluent_english = np.random.randint(4, 10, num_samples)
+reading_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+speaking_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+writing_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+
+# Numeracy and computer skills
+numeracy = np.random.randint(3, 10, num_samples)
+computer = np.random.randint(3, 10, num_samples)
+
+# Binary features generator (0 or 1)
+binary_features = lambda: np.random.randint(0, 2, num_samples)
+
+# Base success rate formula influenced by key features
+success_rate_base = (
+    fluent_english * 2 + 
+    level_of_schooling * 2 +
+    computer * 2 +
+    numeracy * 1.5 +
+    work_experience * 1.2 +
+    (binary_features() * 10)  # bonus random life factors
+)
+
+# Normalize and add Gaussian noise
+success_rate = (success_rate_base / success_rate_base.max() * 100) + np.random.normal(0, 5, num_samples)
+success_rate = np.clip(success_rate.astype(int), 0, 100)
+
+# Create the dataframe
+data = pd.DataFrame({
+    "age": age,
+    "gender": binary_features(),
+    "work_experience": work_experience,
+    "canada_workex": canada_workex,
+    "dep_num": np.random.randint(0, 5, num_samples),
+    "canada_born": binary_features(),
+    "citizen_status": binary_features(),
+    "level_of_schooling": level_of_schooling,
+    "fluent_english": fluent_english,
+    "reading_english_scale": reading_english,
+    "speaking_english_scale": speaking_english,
+    "writing_english_scale": writing_english,
+    "numeracy_scale": numeracy,
+    "computer_scale": computer,
+    "transportation_bool": binary_features(),
+    "caregiver_bool": binary_features(),
+    "housing": np.random.randint(1, 10, num_samples),
+    "income_source": np.random.randint(1, 10, num_samples),
+    "felony_bool": binary_features(),
+    "attending_school": binary_features(),
+    "currently_employed": binary_features(),
+    "substance_use": binary_features(),
+    "time_unemployed": np.random.randint(0, 8, num_samples),
+    "need_mental_health_support_bool": binary_features(),
+    "employment_assistance": binary_features(),
+    "life_stabilization": binary_features(),
+    "retention_services": binary_features(),
+    "specialized_services": binary_features(),
+    "employment_related_financial_supports": binary_features(),
+    "employer_financial_supports": binary_features(),
+    "enhanced_referrals": binary_features(),
+    "success_rate": success_rate
+})
+
+# Save to CSV
+# output_path = os.path.join("app", "clients", "service", "data_commontool_synthetic.csv")
+output_path = os.path.join(os.path.dirname(__file__), "data_commontool_synthetic.csv")
+
+data.to_csv(output_path, index=False)
+
+print(f"✅ {num_samples} synthetic data rows generated and saved to data_commontool_synthetic.csv")
diff --git a/app/clients/service/initial_model_trainers/get_synthetic_testdata.py b/app/clients/service/initial_model_trainers/get_synthetic_testdata.py
new file mode 100644
index 00000000..212b27e7
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/get_synthetic_testdata.py
@@ -0,0 +1,130 @@
+import pandas as pd
+import numpy as np
+import os
+
+np.random.seed(42)  # For reproducibility
+
+num_samples = 4000  # Number of rows
+
+# Age distribution: Normal distribution centered around 30, capped between 18 and 60
+age = np.random.normal(loc=30, scale=8, size=num_samples).astype(int)
+age = np.clip(age, 18, 60)
+
+# Work experience based on age (years since 18 minus some gap years)
+work_experience = (age - 18) - np.random.randint(0, 4, num_samples)
+work_experience = np.clip(work_experience, 0, 42)
+
+# Canadian work experience is a fraction of total work experience
+canada_workex = (work_experience * np.random.uniform(0.3, 0.9, num_samples)).astype(int)
+
+# Education level: weighted choice
+level_of_schooling = np.random.choice([6, 8, 10, 12, 14], size=num_samples, p=[0.1, 0.25, 0.3, 0.25, 0.1])
+
+# English proficiency
+fluent_english = np.random.randint(4, 10, num_samples)
+reading_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+speaking_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+writing_english = np.clip(fluent_english + np.random.randint(-1, 2, num_samples), 1, 10)
+
+# Numeracy and computer skills
+numeracy = np.random.randint(3, 10, num_samples)
+computer = np.random.randint(3, 10, num_samples)
+
+# Binary features generator
+binary_features = lambda: np.random.randint(0, 2, num_samples)
+
+# Other categorical and binary features
+gender = binary_features()
+dep_num = np.random.randint(0, 5, num_samples)
+canada_born = binary_features()
+citizen_status = binary_features()
+transportation_bool = binary_features()
+caregiver_bool = binary_features()
+housing = np.random.randint(1, 10, num_samples)
+income_source = np.random.randint(1, 10, num_samples)
+felony_bool = binary_features()
+attending_school = binary_features()
+currently_employed = binary_features()
+substance_use = binary_features()
+time_unemployed = np.random.randint(0, 8, num_samples)
+need_mental_health_support_bool = binary_features()
+employment_assistance = binary_features()
+life_stabilization = binary_features()
+retention_services = binary_features()
+specialized_services = binary_features()
+employment_related_financial_supports = binary_features()
+employer_financial_supports = binary_features()
+enhanced_referrals = binary_features()
+
+# Success Rate Logic
+
+felony_penalty = np.where(felony_bool == 1, -15, 0)
+substance_penalty = np.where(substance_use == 1, -10, 0)
+long_unemployed_penalty = np.where(time_unemployed >= 4, -5, 0)
+
+bonus_services = (
+    (employment_assistance + life_stabilization + retention_services + specialized_services) * 3
+)
+
+employment_bonus = np.where(currently_employed == 1, 5, 0)
+education_effect = np.minimum(level_of_schooling, 12)  # Cap effect after a certain level
+
+success_rate_base = (
+    fluent_english * 2 +
+    education_effect * 2 +
+    computer * 2 +
+    numeracy * 1.5 +
+    work_experience * 1.0 +
+    employment_bonus +
+    bonus_services +
+    felony_penalty +
+    substance_penalty +
+    long_unemployed_penalty +
+    (np.random.randint(0, 2, num_samples) * 5)  # Random life variance
+)
+
+# Normalize and add Gaussian noise
+success_rate = (success_rate_base / success_rate_base.max() * 100) + np.random.normal(0, 5, num_samples)
+success_rate = np.clip(success_rate.astype(int), 0, 100)
+
+# Create DataFrame
+data = pd.DataFrame({
+    "age": age,
+    "gender": gender,
+    "work_experience": work_experience,
+    "canada_workex": canada_workex,
+    "dep_num": dep_num,
+    "canada_born": canada_born,
+    "citizen_status": citizen_status,
+    "level_of_schooling": level_of_schooling,
+    "fluent_english": fluent_english,
+    "reading_english_scale": reading_english,
+    "speaking_english_scale": speaking_english,
+    "writing_english_scale": writing_english,
+    "numeracy_scale": numeracy,
+    "computer_scale": computer,
+    "transportation_bool": transportation_bool,
+    "caregiver_bool": caregiver_bool,
+    "housing": housing,
+    "income_source": income_source,
+    "felony_bool": felony_bool,
+    "attending_school": attending_school,
+    "currently_employed": currently_employed,
+    "substance_use": substance_use,
+    "time_unemployed": time_unemployed,
+    "need_mental_health_support_bool": need_mental_health_support_bool,
+    "employment_assistance": employment_assistance,
+    "life_stabilization": life_stabilization,
+    "retention_services": retention_services,
+    "specialized_services": specialized_services,
+    "employment_related_financial_supports": employment_related_financial_supports,
+    "employer_financial_supports": employer_financial_supports,
+    "enhanced_referrals": enhanced_referrals,
+    "success_rate": success_rate
+})
+
+# Save to CSV
+output_path = os.path.join(os.path.dirname(__file__), "../data_commontool_synthetic_testdata.csv")
+data.to_csv(output_path, index=False)
+
+print(f" {num_samples} diversified synthetic data rows generated and saved to data_commontool_synthetic.csv")
diff --git a/app/clients/service/initial_model_trainers/linear_regression_model.py b/app/clients/service/initial_model_trainers/linear_regression_model.py
new file mode 100644
index 00000000..533742ee
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/linear_regression_model.py
@@ -0,0 +1,89 @@
+import pickle
+import os
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+# Model map for easy switching
+MODEL_MAP = {
+    "linear_regression": LinearRegression()
+}
+
+ALL_FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+LINEAR_MODEL_FEATURES = [
+    'age', 'work_experience', 'canada_workex', 'level_of_schooling', 'fluent_english',
+    'reading_english_scale', 'speaking_english_scale', 'writing_english_scale', 'numeracy_scale', 'computer_scale'
+]
+
+
+# Dynamically get the current script's directory (this will always work regardless of where you run from)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Path to CSV file using relative path
+DATA_PATH = os.path.join(BASE_DIR, '../../clients/service/data_commontool_synthetic.csv')
+
+# Path to output model directory
+MODEL_DIR = os.path.join(BASE_DIR, '../../clients/service/MLmodels/')
+os.makedirs(MODEL_DIR, exist_ok=True)  # auto create if not exists
+
+def load_data(selected_features):
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def train_and_evaluate_with_split(model_name, selected_features):
+    X, y = load_data(selected_features)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = MODEL_MAP[model_name]
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+    return model  # return model so you can save it later
+
+def cross_validate_model(model_name, selected_features):
+    X, y = load_data(selected_features)
+    model = MODEL_MAP[model_name]
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)  # 5-fold CV with shuffling
+    r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ Model saved as {model_path}")
+
+def main():
+    print("=== Linear Regression on ALL_FEATURES ===")
+    train_and_evaluate_with_split("linear_regression", ALL_FEATURES)
+    cross_validate_model("linear_regression", ALL_FEATURES)
+    
+    print("\n=== Linear Regression on LINEAR_MODEL_FEATURES ===")
+    model = train_and_evaluate_with_split("linear_regression", LINEAR_MODEL_FEATURES)
+    cross_validate_model("linear_regression", LINEAR_MODEL_FEATURES)
+    
+    # Save model trained on linear subset
+    save_model(model, "linear_model_subset.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/initial_model_trainers/model_results.py b/app/clients/service/initial_model_trainers/model_results.py
new file mode 100644
index 00000000..6073360e
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/model_results.py
@@ -0,0 +1,55 @@
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+models = [
+    '../MLmodels/linear_model_subset.pkl',
+    '../MLmodels/ridge_model.pkl',
+    '../MLmodels/xgboost_model.pkl',
+    '../MLmodels/model.pkl'
+]
+
+# Load data
+# data = pd.read_csv('../data_commontool_synthetic.csv')
+data = pd.read_csv('../data_commontool_synthetic_testdata.csv')
+
+# Feature set for linear_model_subset.pkl
+LINEAR_MODEL_FEATURES = [
+    'age', 'work_experience', 'canada_workex', 'level_of_schooling', 'fluent_english',
+    'reading_english_scale', 'speaking_english_scale', 'writing_english_scale', 'numeracy_scale', 'computer_scale'
+]
+
+# Targets
+y = data['success_rate'].values
+
+def load_model(filename):
+    with open(filename, "rb") as model_file:
+        return pickle.load(model_file)
+
+def main():
+    print("========== Starting model testing ==========\n")
+    for model_path in models:
+        loaded_model = load_model(model_path)
+        model_name = model_path.split('/')[-1]
+        print(f">>> Testing Model: {model_name}")
+        
+        # Choose feature set depending on model
+        if 'linear_model_subset' in model_path:
+            X = data[LINEAR_MODEL_FEATURES].values
+        else:
+            X = data.drop(columns=['success_rate']).values
+
+        predictions = loaded_model.predict(X)
+        mae = mean_absolute_error(y, predictions)
+        mse = mean_squared_error(y, predictions)
+        r2 = r2_score(y, predictions)
+        print(f"    MAE: {mae:.2f}")
+        print(f"    MSE: {mse:.2f}")
+        print(f"    R2:  {r2:.2f}")
+        print("-" * 40)
+        
+    print("\n========== Model testing completed ==========")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/initial_model_trainers/ridge_regression_mode.py b/app/clients/service/initial_model_trainers/ridge_regression_mode.py
new file mode 100644
index 00000000..343a90ab
--- /dev/null
+++ b/app/clients/service/initial_model_trainers/ridge_regression_mode.py
@@ -0,0 +1,83 @@
+import os
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.linear_model import Ridge
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+# Define feature set
+FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+# Set relative paths
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(BASE_DIR, '../data_commontool_synthetic.csv')
+MODEL_DIR = os.path.join(BASE_DIR, '..//MLmodels/')
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+def load_data(selected_features):
+    """Load dataset and return features and targets"""
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def train_ridge(X_train, y_train):
+    """Train Ridge Regression with alpha=1.0"""
+    model = Ridge(alpha=1.0)
+    model.fit(X_train, y_train)
+    return model
+
+def evaluate_model(model, X_test, y_test):
+    """Evaluate model on the hold-out test set"""
+    predictions = model.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+
+def cross_validate(model, X, y):
+    """Perform 5-fold cross-validation"""
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    """Save trained model as a pickle file"""
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ Ridge model saved at {model_path}")
+
+def main():
+    # Load data
+    X, y = load_data(FEATURES)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    # Train Ridge model
+    model = train_ridge(X_train, y_train)
+    
+    # Evaluate on hold-out test set
+    evaluate_model(model, X_test, y_test)
+    
+    # Cross-validation
+    cross_validate(model, X, y)
+    
+    # Save model
+    save_model(model, "ridge_model.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/model.py b/app/clients/service/model.py
index b2406370..2c4c3ae1 100644
--- a/app/clients/service/model.py
+++ b/app/clients/service/model.py
@@ -107,4 +107,4 @@ def main():
     print("Model training completed and saved successfully.")
 
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file
diff --git a/app/clients/service/tuned_model_trainers/XGBoost_model_final.py b/app/clients/service/tuned_model_trainers/XGBoost_model_final.py
new file mode 100644
index 00000000..6dc4c66c
--- /dev/null
+++ b/app/clients/service/tuned_model_trainers/XGBoost_model_final.py
@@ -0,0 +1,103 @@
+import os
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV, KFold,cross_val_score
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from xgboost import XGBRegressor
+
+
+
+# Define feature set
+FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(BASE_DIR, '../data_commontool_synthetic.csv')
+MODEL_DIR = os.path.join(BASE_DIR, '../final_MLmodels/')
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+def load_data(selected_features):
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def build_pipeline():
+    xgb = XGBRegressor(objective='reg:squarederror', random_state=42, n_jobs=-1)
+    pipeline = Pipeline([
+        ('scaler', StandardScaler()),
+        ('xgb', xgb)
+    ])
+    return pipeline
+
+def grid_search_tune(X_train, y_train):
+    pipeline = build_pipeline()
+
+    param_grid = {
+        'xgb__n_estimators': [100, 200, 300],
+        'xgb__max_depth': [4, 6, 8],
+        'xgb__learning_rate': [0.01, 0.05, 0.1],
+        'xgb__subsample': [0.7, 0.8, 1.0],
+        'xgb__colsample_bytree': [0.7, 0.8, 1.0],
+        'xgb__reg_alpha': [0, 0.5, 1.0],
+        'xgb__reg_lambda': [1.0, 2.0]
+    }
+
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    grid = GridSearchCV(pipeline, param_grid, cv=cv, scoring="r2", verbose=2, n_jobs=-1)
+    grid.fit(X_train, y_train)
+    
+    print(f"✅ Best params: {grid.best_params_}")
+    return grid.best_estimator_
+
+def evaluate_model(model, X_test, y_test):
+    predictions = model.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+
+def cross_validate(model, X, y):
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ Tuned XGBoost model saved at {model_path}")
+
+def main():
+    X, y = load_data(FEATURES)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # GridSearch to get best pipeline
+    model = grid_search_tune(X_train, y_train)
+    
+    # Evaluate on hold-out
+    evaluate_model(model, X_test, y_test)
+    
+    # Full CV scores
+    cross_validate(model, X, y)
+    
+    # Save final model
+    save_model(model, "final_xgboost_gridcv_model.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/tuned_model_trainers/final_model_results.py b/app/clients/service/tuned_model_trainers/final_model_results.py
new file mode 100644
index 00000000..7643bf10
--- /dev/null
+++ b/app/clients/service/tuned_model_trainers/final_model_results.py
@@ -0,0 +1,55 @@
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+models = [
+    '../final_MLmodels/final_linear_model_subset.pkl',
+    '../final_MLmodels/final_ridge_model.pkl',
+    '../final_MLmodels/final_xgboost_gridcv_model.pkl',
+    '../MLmodels/model.pkl'
+]
+
+# Load data
+# data = pd.read_csv('../data_commontool_synthetic.csv')
+data = pd.read_csv('../data_commontool_synthetic_testdata.csv')
+
+# Feature set for linear_model_subset.pkl
+LINEAR_MODEL_FEATURES = [
+    'age', 'work_experience', 'canada_workex', 'level_of_schooling', 'fluent_english',
+    'reading_english_scale', 'speaking_english_scale', 'writing_english_scale', 'numeracy_scale', 'computer_scale'
+]
+
+# Targets
+y = data['success_rate'].values
+
+def load_model(filename):
+    with open(filename, "rb") as model_file:
+        return pickle.load(model_file)
+
+def main():
+    print("========== Starting model testing ==========\n")
+    for model_path in models:
+        loaded_model = load_model(model_path)
+        model_name = model_path.split('/')[-1]
+        print(f">>> Testing Model: {model_name}")
+        
+        # Choose feature set depending on model
+        if 'linear_model_subset' in model_path:
+            X = data[LINEAR_MODEL_FEATURES].values
+        else:
+            X = data.drop(columns=['success_rate']).values
+
+        predictions = loaded_model.predict(X)
+        mae = mean_absolute_error(y, predictions)
+        mse = mean_squared_error(y, predictions)
+        r2 = r2_score(y, predictions)
+        print(f"    MAE: {mae:.2f}")
+        print(f"    MSE: {mse:.2f}")
+        print(f"    R2:  {r2:.2f}")
+        print("-" * 40)
+        
+    print("\n========== Model testing completed ==========")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/tuned_model_trainers/linear_regression_model_final.py b/app/clients/service/tuned_model_trainers/linear_regression_model_final.py
new file mode 100644
index 00000000..58734aaa
--- /dev/null
+++ b/app/clients/service/tuned_model_trainers/linear_regression_model_final.py
@@ -0,0 +1,90 @@
+import pickle
+import os
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+MODEL_MAP = {
+    "linear_regression": LinearRegression()
+}
+
+ALL_FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+LINEAR_MODEL_FEATURES = [
+    'age', 'work_experience', 'canada_workex', 'level_of_schooling', 'fluent_english',
+    'reading_english_scale', 'speaking_english_scale', 'writing_english_scale', 'numeracy_scale', 'computer_scale'
+]
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(BASE_DIR, '../data_commontool_synthetic.csv')
+MODEL_DIR = os.path.join(BASE_DIR, '../final_MLmodels/')
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+def load_data(selected_features):
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def build_pipeline(use_poly=False):
+    steps = [('scaler', StandardScaler())]
+    if use_poly:
+        steps.append(('poly', PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)))
+    steps.append(('regressor', MODEL_MAP["linear_regression"]))
+    return Pipeline(steps)
+
+def train_and_evaluate_with_split(selected_features, use_poly=False):
+    X, y = load_data(selected_features)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    pipeline = build_pipeline(use_poly=use_poly)
+    pipeline.fit(X_train, y_train)
+    predictions = pipeline.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+    return pipeline
+
+def cross_validate_model(selected_features, use_poly=False):
+    X, y = load_data(selected_features)
+    pipeline = build_pipeline(use_poly=use_poly)
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    r2_scores = cross_val_score(pipeline, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(pipeline, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ Model saved as {model_path}")
+
+def main():
+    print("=== Linear Regression on ALL_FEATURES (+ poly) ===")
+    model = train_and_evaluate_with_split(ALL_FEATURES, use_poly=True)
+    cross_validate_model(ALL_FEATURES, use_poly=True)
+    
+    print("\n=== Linear Regression on LINEAR_MODEL_FEATURES ===")
+    model_subset = train_and_evaluate_with_split(LINEAR_MODEL_FEATURES, use_poly=False)
+    cross_validate_model(LINEAR_MODEL_FEATURES, use_poly=False)
+    
+    save_model(model_subset, "final_linear_model_subset.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py b/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py
new file mode 100644
index 00000000..f40cc4de
--- /dev/null
+++ b/app/clients/service/tuned_model_trainers/ridge_regression_model_final.py
@@ -0,0 +1,76 @@
+import os
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.linear_model import RidgeCV
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+# Define feature set
+FEATURES = [
+    'age', 'gender', 'work_experience', 'canada_workex', 'dep_num', 'canada_born', 'citizen_status',
+    'level_of_schooling', 'fluent_english', 'reading_english_scale', 'speaking_english_scale',
+    'writing_english_scale', 'numeracy_scale', 'computer_scale', 'transportation_bool', 'caregiver_bool',
+    'housing', 'income_source', 'felony_bool', 'attending_school', 'currently_employed',
+    'substance_use', 'time_unemployed', 'need_mental_health_support_bool',
+    'employment_assistance', 'life_stabilization', 'retention_services', 'specialized_services',
+    'employment_related_financial_supports', 'employer_financial_supports', 'enhanced_referrals'
+]
+
+# Set relative paths
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DATA_PATH = os.path.join(BASE_DIR, '../data_commontool_synthetic.csv')
+MODEL_DIR = os.path.join(BASE_DIR, '../final_MLmodels/')
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+def load_data(selected_features):
+    data = pd.read_csv(DATA_PATH)
+    X = np.array(data[selected_features])
+    y = np.array(data['success_rate'])
+    return X, y
+
+def build_pipeline():
+    pipeline = Pipeline([
+        ('scaler', StandardScaler()),
+        ('ridge', RidgeCV(alphas=[0.01, 0.1, 1.0, 10.0, 100.0], cv=5))
+    ])
+    return pipeline
+
+def evaluate_model(model, X_test, y_test):
+    predictions = model.predict(X_test)
+    mae = mean_absolute_error(y_test, predictions)
+    mse = mean_squared_error(y_test, predictions)
+    r2 = r2_score(y_test, predictions)
+    print(f"Hold-out Test Set - MAE: {mae:.2f} | MSE: {mse:.2f} | R2: {r2:.2f}")
+
+def cross_validate(model, X, y):
+    cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    r2_scores = cross_val_score(model, X, y, cv=cv, scoring="r2")
+    mae_scores = -cross_val_score(model, X, y, cv=cv, scoring="neg_mean_absolute_error")
+    
+    print(f"Cross-Validation R² scores: {r2_scores}")
+    print(f"Mean R²: {np.mean(r2_scores):.3f}")
+    print(f"Cross-Validation MAE scores: {mae_scores}")
+    print(f"Mean MAE: {np.mean(mae_scores):.3f}")
+
+def save_model(model, filename):
+    model_path = os.path.join(MODEL_DIR, filename)
+    with open(model_path, "wb") as f:
+        pickle.dump(model, f)
+    print(f"✅ Ridge model saved at {model_path}")
+
+def main():
+    X, y = load_data(FEATURES)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = build_pipeline()
+    model.fit(X_train, y_train)
+    
+    evaluate_model(model, X_test, y_test)
+    cross_validate(model, X, y)
+    save_model(model, "final_ridge_model.pkl")
+
+if __name__ == "__main__":
+    main()
diff --git a/app/main.py b/app/main.py
index a8e8fa7f..a2d84b71 100644
--- a/app/main.py
+++ b/app/main.py
@@ -5,6 +5,7 @@
 """
 
 from fastapi import FastAPI
+from app.model.router import router as model_router
 from app import models
 from app.database import engine
 from app.clients.router import router as clients_router
@@ -20,6 +21,8 @@
 # Include routers
 app.include_router(auth_router)
 app.include_router(clients_router)
+app.include_router(model_router)  
+
 
 # Configure CORS middleware
 app.add_middleware(
diff --git a/app/model/router.py b/app/model/router.py
new file mode 100644
index 00000000..8c81574e
--- /dev/null
+++ b/app/model/router.py
@@ -0,0 +1,135 @@
+from fastapi import APIRouter, HTTPException, Depends
+from pydantic import BaseModel
+from typing import List
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neural_network import MLPClassifier
+from sqlalchemy.orm import Session
+from app.database import get_db
+from app.models import Client
+import numpy as np
+import os
+import pickle
+
+router = APIRouter(prefix="/model", tags=["Model Management"])
+
+# Define directory for pickled models
+MODEL_DIR = "app/clients/service/MLmodels"
+
+def load_client_training_data(db):
+    clients = db.query(Client).all()
+    X, y = [], []
+
+    for c in clients:
+        try:
+            features = [
+                c.age or 0,
+                c.gender or 0,
+                c.work_experience or 0,
+                c.canada_workex or 0,
+                c.dep_num or 0,
+                c.reading_english_scale or 0,
+                c.speaking_english_scale or 0,
+                c.writing_english_scale or 0,
+                c.numeracy_scale or 0,
+                c.computer_scale or 0,
+            ]
+            label = 1 if c.currently_employed else 0
+            X.append(features)
+            y.append(label)
+        except:
+            continue  # skip bad records
+    return X, y
+
+
+class ModelManager:
+    available_models = {
+        "logistic_regression": LogisticRegression(),
+        "random_forest": RandomForestClassifier(),
+        "neural_network": MLPClassifier()
+    }
+    current_model_name = "logistic_regression"
+    current_model = available_models[current_model_name]
+
+    @classmethod
+    def load_pickled_models(cls):
+        if not os.path.exists(MODEL_DIR):
+            return
+        for file in os.listdir(MODEL_DIR):
+            if file.endswith(".pkl"):
+                model_name = file.replace(".pkl", "")
+                with open(os.path.join(MODEL_DIR, file), "rb") as f:
+                    model = pickle.load(f)
+                    cls.available_models[model_name] = model
+
+    @classmethod
+    def set_model(cls, model_name: str):
+        if model_name not in cls.available_models:
+            raise ValueError("Model not found")
+        cls.current_model_name = model_name
+        cls.current_model = cls.available_models[model_name]
+
+    @classmethod
+    def get_current_model(cls) -> str:
+        return cls.current_model_name
+
+    @classmethod
+    def list_models(cls):
+        return list(cls.available_models.keys())
+
+    @classmethod
+    def predict(cls, input_data: list):
+        try:
+            return cls.current_model.predict(np.array(input_data).reshape(1, -1)).tolist()
+        except Exception as e:
+            return str(e)
+
+
+# Load models when module loads
+ModelManager.load_pickled_models()
+
+
+@router.post("/train_models")
+def train_models(db: Session = Depends(get_db)):
+    X, y = load_client_training_data(db)
+    if len(X) == 0:
+        return {"error": "No training data found in the database."}
+    for model in ModelManager.available_models.values():
+        try:
+            model.fit(X, y)
+        except Exception as e:
+            print(f"Error training {model}: {e}")
+    return {
+        "message": f"Trained {len(ModelManager.available_models)} models on {len(X)} samples."
+    }
+
+
+class PredictRequest(BaseModel):
+    input: List[float]
+
+@router.post("/predict")
+def predict(request: PredictRequest):
+    prediction = ModelManager.predict(request.input)
+    return {"input": request.input, "prediction": prediction}
+
+
+class SetModelRequest(BaseModel):
+    model_name: str
+
+@router.post("/set_model")
+def set_model(request: SetModelRequest):
+    try:
+        ModelManager.set_model(request.model_name)
+        return {"message": f"Switched to model: {request.model_name}"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/current_model")
+def current_model():
+    return {"current_model": ModelManager.get_current_model()}
+
+
+@router.get("/list_models")
+def list_models():
+    return {"available_models": ModelManager.list_models()}
\ No newline at end of file