In this comprehensive project titled "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI," we embarked on a journey to explore, analyze, and predict the bankruptcy status of companies. Our project began with an exploration of the dataset, which involved importing it using Pandas and refining it by removing leading spaces and replacing spaces with underscores in column names to ensure consistency.
To grasp the dataset's characteristics, we delved into categorized features' distributions, allowing us to understand the underlying patterns within the data. This step helped us gain insights into the distribution of attributes across different classes, aiding in feature selection and engineering.
Moving on to the heart of our project, the prediction of company bankruptcy, we employed various machine learning models. Utilizing grid search, we performed hyperparameter tuning to optimize model performance. Our model arsenal included Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), which were evaluated using accuracy, precision, recall, and F1-score.
Transitioning to deep learning, we implemented an Artificial Neural Network (ANN) model. This involved constructing a feed-forward neural network with hidden layers, dropouts, and activation functions. We evaluated the ANN using accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of its classification performance.
Our journey into deep learning continued with the implementation of Long Short-Term Memory (LSTM) networks, which are well-suited for sequence data. We structured the LSTM model with multiple layers and dropouts, evaluating its performance using metrics like accuracy, precision, recall, and F1-score. This marked a pivotal step in predicting company bankruptcy.
Furthermore, we explored Feed-Forward Neural Networks (FNN) for prediction. Constructing a multi-layered architecture with varied dropouts and activation functions, we assessed its classification capabilities using metrics similar to previous models.
Incorporating Recurrent Neural Networks (RNN) added another dimension to our analysis. Building an RNN model with sequential data, we examined its accuracy, precision, recall, and F1-score, highlighting its ability to capture sequential patterns in bankruptcy data.
To comprehensively evaluate our models, we employed a range of metrics including precision, recall, F1-score, and accuracy. These metrics enabled us to gauge not only the overall model performance but also its capability to correctly predict bankrupt and non-bankrupt cases.
Our project also extended into creating a Python GUI using PyQt. This graphical interface facilitated user interaction, allowing them to input data for prediction and view the outcomes through an intuitive interface. This GUI enhanced accessibility and usability, making it easier for users to engage with our models.
In conclusion, our journey through the "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI" project encompassed data exploration, categorized features distribution analysis, model selection, performance evaluation using diverse metrics, and the creation of an interactive GUI. This endeavor combined analytical rigor, machine learning expertise, and user-centric design to provide a comprehensive solution for predicting company bankruptcy.
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