Energy Consumption Prediction using Machine Learning

In this project, we aimed to predict energy consumption in a residential setting using various machine learning models. The dataset provided valuable information about temperature, humidity, pressure, and other environmental factors, which we used to build predictive models.

Project Steps:

Data Preprocessing: We started by loading and exploring the dataset. We handled missing values, checked for outliers, and performed data transformation and feature selection to prepare the data for modeling.

Exploratory Data Analysis (EDA): Through visualizations, we gained insights into the relationships between variables, identified potential correlations, and understood the distribution of energy consumption.

Model Selection: We chose three different machine learning models - Linear Regression, Random Forest Regression, and XGBoost. These models were trained and evaluated to predict energy consumption.

Model Evaluation: We assessed the models using three key evaluation metrics - Mean Squared Error (MSE), R-squared (R2) Score, and Mean Absolute Error (MAE). These metrics provided insights into how well the models were performing.

Hyperparameter Tuning: For each model, we performed hyperparameter tuning using techniques like GridSearch CV to find the best parameters, which improved model performance.

Feature Importance: We analyzed feature importance for Random Forest Regression to understand which variables had the most impact on energy consumption.

Final Model Selection: After comparing the performance of all models, we selected the Random Forest Regression model as our final prediction model due to its better performance on the evaluation metrics.

Deployment Preparation: We saved the trained Random Forest model in a pickle file for potential deployment in real-world applications.

Results:

The Random Forest Regression model provided the best predictive performance, with improved Mean Squared Error, R-squared Score, and Mean Absolute Error compared to the other models. This model demonstrated its potential for accurately predicting energy consumption based on various environmental factors.

Conclusion:

This project successfully showcased the application of machine learning in predicting energy consumption. Through careful preprocessing, model selection, and evaluation, we identified the Random Forest Regression model as the most suitable for this task. The insights gained from feature importance analysis provide valuable information for optimizing energy usage in residential settings.

The code and findings from this project can be found in the GitHub repository, providing a comprehensive resource for those interested in energy consumption prediction using machine learning.