Automating the Machine Learning Process using PyCaret and Streamlit

Sarangpure, Nikhilesh; Dhamde, Vipul; Roge, Ankita; Doye, Janhawi; Patle, Shivam; Tamboli, Sukhad

doi:10.1109/inocon57975.2023.10101357

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…PyCaret can automatically perform many of the tedious and time-consuming tasks involved in machine learning, such as data preprocessing, feature selection, model selection, and hyperparameter tuning. Overall, PyCaret's AutoML capabilities help abstract machine learning, making it more accessible and usable for a wider range of users [38].…”

Section: Pycaretmentioning

confidence: 99%

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Westergaard,

Erden,

Mateo

et al. 2024

Information

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Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

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Section: Pycaretmentioning

confidence: 99%

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Westergaard,

Erden,

Mateo

et al. 2024

Information

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“…The primary focus was on regression models, a subset of supervised machine learning algorithms capable of predicting continuous numerical outcomes. These models, including linear regression, polynomial regression, ridge regression, Lasso regression, support vector regression (SVR), decision tree regression, random forest regression, and gradient-boosting regression, establish relationships between independent and dependent variables to facilitate accurate predictions (Benos et al, 2021;Cedric et al, 2022;Razzaq, 2020;Sarangpure et al, 2023).…”

Section: Drone Datamentioning

confidence: 99%

Assessing Methane Emissions from Paddy Fields Through Environmental and UAV Remote Sensing Variables

Velez,

Alvarez-Mendoza,

Navarro

et al. 2024

Preprint

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Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. Utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, we achieved remarkable results, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the Random Forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the fittest variables for predicting these values in the model. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

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“…To develop a forest fire diagnostic model, we utilized the Python PyCaret module, which is an open-source, low-code machine-learning framework that facilitates the rapid deployment of models after data preparation. PyCaret was designed to simplify the development, assessment, comparison, and deployment of machine learning models, making the process as efficient and effective as possible [41,42]. In PyCaret, the performance of the model was evaluated against seven criteria: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC), Recall, Precision, F1 score, Kappa Value, and Matthews Correlation Coefficient (MCC).…”

Section: Development Of a Forest Fire Diagnostic Modelmentioning

confidence: 99%

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

Roh,

Lee,

et al. 2024

Forests

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Forest fires have devastating effects on extensive forest areas, compromising vital ecological services such as air purification, water conservation, and recreational opportunities, thus posing a significant socioeconomic threat. Furthermore, the risk of forest fires is steadily increasing due to climate change. The most effective method for mitigating forest fire risk is proactive prevention before forest fires can occur by identifying high-risk areas based on land surface conditions. This study aimed to develop a machine learning-based forest fire diagnostic model designed for South Korea, considering both satellite-derived land surface data and anthropogenic factors. For the remote sensing data, VTCI (Vegetation Temperature Condition Index) was used to reflect the land surface dryness. In addition, fire activity maps for buildings, roads and cropland were used to consider the influence of human activities. The forest fire diagnostic model yielded an accuracy of 0.89, demonstrating its effectiveness in predicting forest fire risk. To validate the effectiveness of the model, 92 short-term forest fire risk forecast maps were generated from March to May 2023 with real-time data on forest fire occurrences collected for verification. The results showed that 73% of forest fires were accurately classified within high-risk zones, confirming the operational accuracy of the model. Through the forest fire diagnostic model, we have presented the impact relationships of meteorological, topographical, and environmental data, as well as the dryness index based on satellite images and anthropogenic factors, on forest fire occurrence. Additionally, we have demonstrated the potential uses of surface condition data.

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Automating the Machine Learning Process using PyCaret and Streamlit

Cited by 10 publications

References 20 publications

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

Assessing Methane Emissions from Paddy Fields Through Environmental and UAV Remote Sensing Variables

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

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