This project presents a prediction model developed using Python in Visual Studio Code (VS Code). The model is deployed as an interactive web application using Streamlit, enabling users to input data and receive predictions seamlessly.
The primary objective of this project is to build a robust predictive model by combining multiple machine learning algorithms through stacking. Stacking, or stacked generalization, involves training various base models and a meta-model that integrates their outputs to enhance overall performance. The interactive Streamlit app allows users to engage with the model, providing an intuitive interface for data input and prediction retrieval.
The repository is organized as follows:
data/: Contains datasets used for training and testing the model. models/: Stores serialized versions of the trained base models and the meta-model. app.py: The main Streamlit application script. requirements.txt: Lists all necessary Python packages and their versions. README.md: This file, providing an overview and guidance on using the project.
The development of this project involved several key steps:
Gathered and cleaned the dataset to ensure quality and relevance. Handled missing values and performed feature engineering to enhance model performance.
Selected diverse base models ( Random Forest, Extreme Gradient Boosting, Support Vector Machine, Light Gradient Boosting Machine) to capture various patterns in the data. Trained each base model on the training dataset. Developed a meta-model that takes the outputs of base models as inputs to make final predictions.
Assessed the performance of individual base models and the stacking model using metrics such as accuracy, precision, recall, and F1-score. Conducted cross-validation to ensure the model's robustness and generalizability.
Built an interactive web application using Streamlit to make the model accessible to users. Designed the app interface for user-friendly data input and clear presentation of predictions.
The project relies on the following Python packages: pandas: For data manipulation and analysis. numpy: For numerical computations. scikit-learn: For machine learning algorithms and tools. streamlit: For developing the web application interface.