This project utilizes Long Short-Term Memory (LSTM) networks implemented in TensorFlow to predict electricity consumption based on a dataset with dimensions (2 million, 10). LSTM is a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks.
The goal of this project is to develop a model capable of predicting electricity consumption patterns using LSTM networks. The provided dataset, with dimensions (2 million, 10), is used to train and evaluate the model's performance.
The dataset used in this project contains information related to electricity consumption. The dimensions of the dataset are (2 million, 10), with each row representing a specific instance and each column representing a feature.
Make sure you have the following dependencies installed:
- Python (>=3.6)
- TensorFlow (>=2.0)
- NumPy
- Pandas
- Matplotlib (for visualizations, optional)
- Jupyter Notebook (for running provided notebooks, optional)
Install the dependencies using:
pip install -r requirements.txt
# Electricity_Consumption