Create demand_prediction_model.py

Author: KOSASIH

models/demand_prediction_model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+import joblib
+import numpy as np
+
+# Load your dataset
+# For demonstration, we will create a synthetic dataset
+# In a real scenario, you would load your dataset from a CSV or database
+data = {
+    'historical_demand': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550],
+    'seasonality': [1, 1, 1, 2, 2, 2, 3, 3, 3, 4],  # Example feature
+    'promotion': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]  # Example feature: promotional activity
+}
+
+df = pd.DataFrame(data)
+
+# Features and target variable
+X = df[['historical_demand', 'seasonality', 'promotion']]
+y = df['historical_demand'].shift(-1).dropna()  # Predict next period's demand
+X = X[:-1]  # Align X with y
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model selection and hyperparameter tuning using GridSearchCV
+param_grid = {
+    'n_estimators': [50, 100, 200],
+    'max_depth': [None, 10, 20, 30],
+    'min_samples_split': [2, 5, 10]
+}
+
+# Using Random Forest Regressor for better performance
+model = RandomForestRegressor(random_state=42)
+
+# Grid search for hyperparameter tuning
+grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=-1)
+grid_search.fit(X_train, y_train)
+
+# Best model from grid search
+best_model = grid_search.best_estimator_
+
+# Evaluate the model
+y_pred = best_model.predict(X_test)
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+print(f'Mean Squared Error: {mse}')
+print(f'R^2 Score: {r2}')
+
+# Save the model
+joblib.dump(best_model, 'models/demand_prediction_model.pkl')
+print('Demand prediction model saved as demand_prediction_model.pkl')