100% ACCURACY Earthquake Type Prediction - Data Every Day #058
Gabriel Atkin・2 minutes read
A data set of significant earthquakes from 1965 to 2016 with 21 attributes is used to predict if an earthquake is automatic or reviewed using TensorFlow artificial neural network, involving importing standard libraries, data preprocessing, feature engineering, and model building. The model achieves a perfect AUC of 1.0 on the test set after training, correctly classifying all examples due to the combination of features rather than a single predictor.
Insights
- Feature engineering plays a crucial role in preparing the earthquake data for analysis, involving the creation of new columns for month and year, extraction of the hour from the time column, and dropping irrelevant columns to enhance the model's predictive capabilities.
- The use of artificial neural networks, specifically TensorFlow, in earthquake classification showcases the power of machine learning techniques in accurately predicting earthquake status (automatic or reviewed) by leveraging extensive data preprocessing, feature engineering, and model training, ultimately achieving outstanding performance with an AUC of 1.0 on the test set.
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Recent questions
How was the earthquake data preprocessed?
The earthquake data was preprocessed by handling missing values through imputing the mean for the "root mean square" column and dropping rows with missing values in the "magnitude type" column. Additionally, feature engineering was conducted by creating new columns for month and year from the "date" column, converting them to integers, and extracting the hour from the "time" column to convert it to integers.
What libraries were used for data processing?
The standard libraries used for data processing and visualization included numpy, pandas, matplotlib, and seaborn. These libraries were essential for importing, processing, and visualizing the earthquake data set to prepare it for model building and analysis.
How was the earthquake data visualized?
The earthquake data was visualized using seaborn, with a heatmap of correlations and kernel density estimation plots for numeric columns. The heatmap displayed correlation values between different attributes, while the kernel density estimation plots helped in understanding the distributions of each feature in the data set.
What encoding technique was applied to text columns?
One-hot encoding was applied to text columns with more than two unique values. This technique created dummy columns for each unique value in the text columns, ensuring that the data was appropriately encoded for further processing and analysis.
How was the earthquake prediction model evaluated?
The earthquake prediction model was evaluated using an Adam optimizer, binary cross entropy as the loss function, and the AUC metric to assess performance across classes and classification thresholds. The model was trained with a batch size of 32, 30 epochs, and a callback function to aid convergence, resulting in a high AUC of 1.0 on the test set, correctly classifying all 7022 examples.
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