Depression Detection system with Statistical Analysis and Data Mining Approaches

Hassan, Md. Mehedi; Khan, Md. Asif Rakib; Islam, Khan Kamrul; Hassan, Md. Mahedi; Rabbi, M.M. Fazle

doi:10.1109/icsct53883.2021.9642550

Cited by 6 publications

(1 citation statement)

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“…The goal of the research was to determine the most suitable classification model for predicting whether it will rain tomorrow, based on a range of features extracted from the dataset. By employing effective feature engineering techniques, the researchers aimed to identify the most infor-mative variables that would contribute to the construction of highly accurate prediction models [15].The data collection period spans from 01-12-2008 to 25-06-2017, during which the Australian Bureau of Meteorology automatically collected data from 49 weather stations. The dataset contains a total of 23 distinct features and 145,460 observations.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness

Hassan,

Rony,

Khan

et al. 2023

IEEE Access

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Rainfall prediction plays a crucial role in raising awareness about the potential dangers associated with rain and enabling individuals to take proactive measures for their safety. This study aims to utilize machine learning algorithms to accurately predict rainfall, considering the significant impact of scarcity or extreme rainfall on both rural and urban life. The complex nature of rainfall, influenced by various atmospheric, oceanic, and geographical factors, makes it a challenging phenomenon to forecast. This research employs data preprocessing techniques, outlier analysis, correlation analysis, feature selection, and several machine learning algorithms such as Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. The study focuses on developing the most accurate rainfall prediction model by utilizing machine learning and feature selection techniques. The Artificial Neural Network (ANN) achieves a maximum accuracy of 90% and 91% before and after feature selection, respectively. Furthermore, k-means clustering and Principal Component Analysis (PCA) are applied to examine regional rainfall patterns in Australia. Lastly, to make our proposed machine learning simpler and more usable for general people, we have formulated a web-based application system using Flask in our research paper. Overall, this research demonstrates the effectiveness of different machine-learning techniques in predicting rainfall using Australian weather data.

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Section: A Dataset Descriptionmentioning

confidence: 99%