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This dataset contains about 10 years of daily weather observations from many locations across Australia. RainTomorrow is the target variable to predict. You will determine rain or not in the next day. This column is Yes if the rain for that day was 1mm or more. Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data.

The dataset contains 23 attributes. Some of them are as follows: About some of them are: DATE - The date of observation; LOCATION - The common name of the location of the weather station; MINTEMP - The minimum temperature in degrees celsius; MAXTEMP - The maximum temperature in degrees celsius; RAINFALL - The amount of rainfall recorded for the day in mm; EVAPORATION - The so-called Class A pan evaporation (mm) in the 24 hours to 9am; SUNSHINE - The number of hours of bright sunshine in the day; WINDGUESTDIR - The direction of the strongest wind gust in the 24 hours to midnight; WINDGUESTSPEED- The speed (km/h) of the strongest wind gust in the 24 hours to midnight; and WINDDIR9AM - Direction of the wind at 9am.

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.

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The dataset used in this book consists of daily weather observations from various locations in Australia spanning a 10-year period. The target variable is "RainTomorrow," which predicts whether it will rain the following day.

The dataset comprises 23 attributes, including: DATE: The date of observation.; LOCATION: The name of the weather stations location.; MINTEMP: The minimum temperature in degrees Celsius.; MAXTEMP: The maximum temperature in degrees Celsius.; RAINFALL: The amount of rainfall recorded for the day in mm.; EVAPORATION: Class A pan evaporation in mm for the 24 hours until 9 am.; SUNSHINE: The number of hours of bright sunshine in a day.; WINDGUSTDIR: The direction of the strongest wind gust in the 24 hours until midnight.; WINDGUSTSPEED: The speed of the strongest wind gust in km/h in the 24 hours until midnight.; WINDDIR9AM: The direction of the wind at 9 am.

The project utilizes several machine learning models, including K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling techniques, namely raw scaling, MinMax scaling, and standard scaling, are employed. These machine learning models are utilized to analyze the weather attributes and make predictions about the occurrence of rainfall. Each model has its strengths and may perform differently based on the characteristics of the dataset.

Additionally, a GUI is developed using PyQt5 to visualize cross-validation scores, predicted values versus true values, confusion matrix, learning curves, decision boundaries, model performance, scalability, training loss, and training accuracy. These visualizations within the GUI provide a comprehensive understanding of the models performance, learning behavior, decision-making boundaries, and the quality of its predictions. Users can leverage these insights to fine-tune the model and improve its accuracy and generalization capabilities. In addition, the GUI developed using PyQt5 also includes the capability to visualize features on a year-wise and month-wise basis. This functionality allows users to explore the variations and trends in different weather attributes across different years and months. With the year-wise and month-wise visualizations, users can gain insights into the temporal patterns and trends present in the weather data. It enables them to observe how specific attributes change over time and across different seasons, providing a deeper understanding of the weather patterns and their potential influence on rainfall occurrences.

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DATA SCIENCE FOR RAIN CLASSIFICATION AND PREDICTION WITH PYTHON GUI

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