In this notebook I use linear regression to predict energy consumption based on various features like temperature, humidity, and wind speed.

The notebook starts with an exploratory data analysis (EDA) section, where I create visualizations and statistical summaries of the dataset to identify patterns and relationships between the different variables.After the EDA, I preprocess the data by handling missing values and splitting it into training and testing sets. Then I build a linear regression model to predict energy consumption based on the features.

The data modelling in this document followed the steps below:  

» Data Preparation: Import libraries, load and preprocess data, split into training and testing datasets.  

» Exploratory Data Analysis (EDA): Explore data using visualizations and statistical techniques to understand relationships and identify outliers.  

» Feature Selection: Select relevant features using domain knowledge and statistical techniques.  

» Model Training: Train a linear regression model to predict energy consumption based on selected features.  

» Model Evaluation: Evaluate the model using testing dataset and metrics like mean squared error and R-squared value.  

» Model Improvement: Improve the model by tuning hyperparameters and adding new features through an iterative process of retraining and evaluation.

Finally, I evaluate the model's performance using metrics like mean absolute error (MAE) and root mean squared error (RMSE). Providing visualizations to help interpret the results.

Techniques

Python

Linear Regression