YigitTurali / Shell_App

Shell_App is an integrated data analysis and modeling platform, featuring scripts for EDA, ensemble techniques, LightGBM, and SARIMAX modeling. Designed for comprehensive data processing, the repository also includes deployment capabilities via Streamlit for interactive model interactions.

Shell_App

Shell_App is a comprehensive repository that contains various Python scripts designed for data analysis, modeling, and application deployment. The repository is structured with multiple Python files, each serving a specific purpose in the data processing and modeling pipeline.

Repository Structure

• Shell_EDA.py: This script is dedicated to Exploratory Data Analysis (EDA). It contains functions and methods to analyze the dataset, visualize data distributions, and gain insights into the underlying patterns of the data.

• Shell_Ensemble.py: As the name suggests, this script is focused on ensemble modeling techniques. It provides functionalities to combine predictions from multiple models to improve the overall prediction accuracy.

• Shell_LightGBM.py: This script is centered around the LightGBM model, a gradient boosting framework that uses tree-based algorithms. It contains methods to train, evaluate, and make predictions using the LightGBM model.

• Shell_SARIMAX.py: This script is dedicated to the SARIMAX model, which stands for Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors. It's a time series forecasting model that can handle seasonality, trend, and exogenous variables.

• main.py: This is the primary script that integrates all the functionalities from the other scripts. It orchestrates the flow of data processing, modeling, and prediction.

• streamlit_app.py: This script is designed to deploy the application using Streamlit, a popular open-source app framework for Machine Learning and Data Science projects. It provides an interactive user interface for users to interact with the models and view predictions.

Usage

1. Clone the repository to your local machine.
2. Ensure you have all the necessary libraries and dependencies installed.
3. Run the main.py script to initiate the data processing and modeling pipeline.
4. To deploy the application, run the streamlit_app.py script.

Contribution

Feel free to fork the repository, make changes, and submit pull requests. Any contributions to improve the code or add new features are welcome!