🏠 Cleaned UCI Household Energy Consumption Dataset

This repository contains a cleaned and preprocessed version of the UCI Individual Household Electric Power Consumption Dataset. The data records the electric power usage of a single household between December 2006 and November 2010, with measurements taken every minute.

ℹ️ Original Dataset Information

Source: UCI Machine Learning Repository Time Period: December 2006 to November 2010 (47 months) Sampling Rate: 1 minute Number of Measurements: 2,075,259 measurements originally

📊 Dataset Overview Column Description Unit DateTime Combined date and time column — Global_active_power Total active power consumed kilowatts (kW) Global_reactive_power Reactive power kilowatts (kW) Voltage Average voltage volts (V) Global_intensity Current intensity amperes (A) Sub_metering_1 Energy sub-metering 1 (kitchen) watt-hour (Wh) Sub_metering_2 Energy sub-metering 2 (laundry) watt-hour (Wh) Sub_metering_3 Energy sub-metering 3 (AC & water heater) watt-hour (Wh)

🧹 Cleaning Steps Performed

Combined Date and Time columns into a single DateTime column.

Replaced missing values (?) with NaN and removed incomplete rows.

Converted numeric columns to the correct float data type.

Set DateTime as index for time-series analysis.

Saved cleaned dataset as a CSV file for direct use in analysis or ML models.

📦 Files Included

household_energy_cleaned.csv → Final cleaned dataset

cleaning_script.py → Python script used for data cleaning

README.md → This documentation file

⚙️ Example Usage import pandas as pd

Load cleaned dataset

df = pd.read_csv('household_energy_cleaned.csv', parse_dates=['DateTime'], index_col='DateTime')

Display info

print(df.info()) print(df.head())

🧠 Potential Use Cases

Energy consumption forecasting

Anomaly detection in power usage

Time-series analysis

Renewable energy optimization research

🏷️ Source

Original data: UCI Machine Learning Repository – Individual household electric power consumption

📜 License

This cleaned dataset is released for educational and research purposes only. Please cite the original UCI dataset if used in publications.

🤝 Contributing Contributions are welcome! Please feel free to submit a Pull Request.

👩‍💻 Author

Maintained by Sayyed Mehwish, Computer Science Engineering student. This repository demonstrates dataset cleaning and preparation for ML-based energy forecasting projects.