The goal of this project is to predict monthly microbusiness density in a given area. Developing an accurate model trained on U.S. county-level data.

Dataset can be obtained from kaggle competition. In this repository I use kaggle API to pull the dataset kaggle competitions download -c godaddy-microbusiness-density-forecasting

The notebook.ipynb file contains the code used to preprocess the data, build the model, train it, and generate forecasts. The notebook is divided into the following sections:

• Data Loading and Exploration
• Data Preprocessing
• Model Architecture
• Model Training
• Model Evaluation and Forecasting

The model used in this project is a deep learning model based on Long Short-Term Memory (LSTM) neural networks. The model consists of 6 LSTM layers, followed by a dense layer that outputs the forecast. The model is trained using the mean absolute error (MAE) loss function and the Adam optimizer.

In this project, my main focus was on building the machine learning model and gaining a better understanding of the overall flow of an ML project. As a result, there is limited information regarding why I chose a specific architecture for the model or the feature selection process. Moving forward, I plan to dedicate more time to improving the accuracy of the model and conducting a more in-depth analysis of both the dataset and the various model selection strategies available.