This project is a deep learning model built using Long Short-Term Memory (LSTM) to predict future cashflows for a business based on historical data. The model is
trained on a dataset of past cashflows, and it can predict future cashflows with a high degree of accuracy.
Dataset
The dataset used for this project contains the following features:
Date Revenue Expenses Cashflow
Model:
The model is built using LSTM, which is a type of recurrent neural network (RNN) that can process sequential data. The model is trained on the past cashflow data to
learn the patterns and relationships between the different features. Once trained, the model can predict future cashflows based on the current and past data.
Technologies Used:
Python
Pandas Library
TensorFlow Library
Keras Library
Scikit-Learn Library
Installation:
pip install pandas
pip install tensorflow
pip install keras
pip install scikit-learn
Project Structure
├── Cashflow-Prediction-using-LSTM.ipynb
├── data
│ └── cashflow_data.csv
└── README.md
Conclusion
This project demonstrates the potential of using LSTM to predict future cashflows for a business. By leveraging the power of deep learning, we were able to build a
model that can accurately predict future cashflows based on past data. This model can be extended to include more features and can be used by businesses to better
forecast their financial performance.