Data cleaning

[dfrancis-tech]

In our email spam detection project, it has become evident that the quality and cleanliness of our training data play a crucial role in the performance of our spam detection models. This GitHub issue aims to address the need for data-cleaning techniques to enhance the quality and reliability of our training dataset.

Goals:

Data Standardization: Develop procedures to standardize the format, structure, and encoding of the email data. This includes normalizing text, removing unnecessary formatting, and ensuring consistent representation across different email sources.

Handling Missing Data: Identify and handle instances of missing or incomplete data within the email dataset. Explore techniques such as imputation or data augmentation to fill in missing values where appropriate.

Removing Duplicate Entries: Implement mechanisms to detect and eliminate duplicate emails from the dataset. Duplicate emails can bias the training process and lead to overfitting, affecting the performance of the spam detection models.

Noise and Outlier Detection: Identify and filter out noisy or outlier emails that may negatively impact the training process. This could involve techniques such as outlier detection algorithms, statistical analysis, or domain-specific knowledge to identify and exclude irrelevant or unusual emails.

Data Balancing: Evaluate the balance between spam and non-spam (ham) emails in the dataset. If significant class imbalance exists, consider techniques such as oversampling, undersampling, or synthetic data generation to address the issue and ensure a more balanced training set.

Manual Annotation and Verification: Consider involving human annotators to review and validate a subset of the data to ensure its accuracy, especially in cases where automated cleaning techniques may not be sufficient.

Tasks:

• Develop data cleaning scripts or functions to standardize email data formats and remove unnecessary formatting.
• Implement algorithms or approaches to handle missing data, such as imputation or data augmentation.
• Design and apply methods to identify and remove duplicate entries from the dataset.
• Explore techniques for noise and outlier detection to filter out irrelevant or abnormal emails.
• Evaluate the class balance in the dataset and apply appropriate techniques to address any significant imbalance.
• Set up a process for manual annotation and verification of a subset of the data to ensure its accuracy and reliability.

Expected Outcome: By applying robust data cleaning techniques, we aim to improve the quality and reliability of our training dataset for email spam detection. This will result in more accurate and efficient spam detection models, leading to better protection for our users' inboxes.

[dfrancis-tech]

Data cleaning is performed in EDA.ipynb and it is completed.