Performing thorough exploratory data analysis (EDA) is a critical step in understanding the characteristics and patterns within our email spam detection dataset. This GitHub issue aims to address the need for comprehensive EDA to gain insights into the data, identify trends, and inform subsequent modeling and feature engineering decisions.
Goals:
Data Profiling: Generate descriptive statistics, summary metrics, and visualizations to gain an overview of the dataset. This includes analyzing the distribution of spam and non-spam emails, examining the class balance, and identifying potential data quality issues.
Feature Analysis: Explore the characteristics and distribution of individual features within the dataset. Analyze the distribution of email lengths, identify common keywords or phrases in spam emails, and compare the content of spam and non-spam emails to identify discriminative features.
Correlation Analysis: Investigate correlations between different features or attributes within the dataset. Identify relationships between email content, metadata, and spam classification to uncover potential patterns or insights that can guide feature selection and engineering.
Visualization: Utilize various visualization techniques such as histograms, scatter plots, word clouds, and box plots to visually represent the data. This aids in identifying outliers, understanding feature distributions, and spotting any inherent patterns or anomalies.
Data Quality Assessment: Assess the quality and integrity of the dataset, identifying missing values, outliers, or inconsistencies that need to be addressed during the data cleaning process.
Feedback Loop: Share interesting findings, visualizations, and insights with the team to foster collaboration and facilitate discussions on potential modeling strategies and feature engineering approaches.
Tasks:
Generate descriptive statistics and summary metrics for the dataset, including class distribution and data quality assessment.
Visualize the distribution of email lengths, word frequencies, and other relevant features.
Conduct feature analysis to identify discriminative keywords or phrases in spam emails.
Perform correlation analysis to identify relationships between different features.
Explore the dataset using various visualization techniques to gain insights.
Document and share interesting findings, visualizations, and insights with the team.
Expected Outcome:
By conducting thorough exploratory data analysis, we aim to gain a deeper understanding of the dataset, identify key patterns and trends, and inform subsequent modeling and feature engineering decisions. This will contribute to the development of more effective and accurate email spam detection models.
dfrancis-tech
commented
1 year ago
Performing thorough exploratory data analysis (EDA) is a critical step in understanding the characteristics and patterns within our email spam detection dataset. This GitHub issue aims to address the need for comprehensive EDA to gain insights into the data, identify trends, and inform subsequent modeling and feature engineering decisions.
Goals:
Tasks:
Expected Outcome:
By conducting thorough exploratory data analysis, we aim to gain a deeper understanding of the dataset, identify key patterns and trends, and inform subsequent modeling and feature engineering decisions. This will contribute to the development of more effective and accurate email spam detection models.