\u6d41\u91cf\u9884\u6d4b\u7684\u6280\u672f\u65b9\u6cd5<\/strong><\/h2>\n\u73b0\u4ee3\u6d41\u91cf\u9884\u6d4b\u9700\u8981\u590d\u6742\u7684\u5206\u6790\u5de5\u5177\u548c\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Python\u8fdb\u884c\u7cbe\u786e\u5e26\u5bbd\u9884\u6d4b\u7684\u7efc\u5408\u65b9\u6cd5:<\/p>\n
\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import mean_squared_error\r\nimport datetime as dt\r\n\r\nclass BandwidthPredictor:\r\n def __init__(self):\r\n self.model = LinearRegression()\r\n self.scaler = None\r\n \r\n def prepare_features(self, df):\r\n df['hour'] = pd.to_datetime(df['timestamp']).dt.hour\r\n df['day_of_week'] = pd.to_datetime(df['timestamp']).dt.dayofweek\r\n df['is_weekend'] = df['day_of_week'].isin([5,6]).astype(int)\r\n df['is_business_hours'] = df['hour'].between(9, 17).astype(int)\r\n return df\r\n \r\n def predict_bandwidth(self, historical_data):\r\n # Convert data to DataFrame\r\n df = pd.DataFrame(historical_data, columns=['timestamp', 'bandwidth_usage'])\r\n \r\n # Feature engineering\r\n df = self.prepare_features(df)\r\n \r\n # Prepare features for modeling\r\n features = ['hour', 'day_of_week', 'is_weekend', 'is_business_hours']\r\n X = df[features]\r\n y = df['bandwidth_usage']\r\n \r\n # Train\/test split\r\n X_train, X_test, y_train, y_test = train_test_split(\r\n X, y, test_size=0.2, random_state=42\r\n )\r\n \r\n # Train model\r\n self.model.fit(X_train, y_train)\r\n \r\n # Calculate accuracy\r\n predictions = self.model.predict(X_test)\r\n mse = mean_squared_error(y_test, predictions)\r\n \r\n return {\r\n 'model': self.model,\r\n 'mse': mse,\r\n 'feature_importance': dict(zip(features, self.model.coef_))\r\n }\r\n\r\n# Usage example\r\nhistorical_data = [\r\n ['2024-01-01 00:00:00', 50],\r\n ['2024-01-01 01:00:00', 45],\r\n ['2024-01-01 02:00:00', 30],\r\n # Add more historical data points\r\n]\r\n\r\npredictor = BandwidthPredictor()\r\nresults = predictor.predict_bandwidth(historical_data)\r\n<\/code><\/pre>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n