└── code


/code:
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 1 | import pandas as pd
 2 | import numpy as np
 3 | from sklearn.model_selection import train_test_split
 4 | from sklearn.linear_model import LinearRegression
 5 | from sklearn.metrics import mean_absolute_error, mean_squared_error
 6 | import matplotlib.pyplot as plt
 7 | 
 8 | # Load dataset
 9 | data = pd.read_csv('energy_consumption.csv')  # Replace with your dataset path
10 | 
11 | # Preprocess data (assuming the dataset has 'date' and 'energy_consumption' columns)
12 | data['date'] = pd.to_datetime(data['date'])
13 | data.set_index('date', inplace=True)
14 | data.sort_index(inplace=True)
15 | 
16 | # Feature engineering (you can add more features as needed)
17 | data['month'] = data.index.month
18 | data['day'] = data.index.day
19 | data['hour'] = data.index.hour
20 | 
21 | # Target variable
22 | X = data[['month', 'day', 'hour']]
23 | y = data['energy_consumption']
24 | 


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