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narittt · Jun 21, 2023 · 71c58f4 · 71c58f4
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+# Email Spam Filter
+
+This project creates an email spam filter based on supervised learning that classifies emails as either spam (unwanted) or ham (legitimate) for my data analysis and vsiualization class.
+
+I used two supervised learning algorithms, K Nearest Neighbors (KNN) and Naive Bayes, and compared their performances. To train and evaluate these classifiers, I used the Enron spam email dataset, which consists of approximately 34,000 emails. Once the classifiers were trained, I ran them in a Jupyter Notebook to predict whether new emails are spam or ham.
+
+## Goals
+
+- Explore and implement the KNN and Naive Bayes algorithms.
+- Gain hands-on experience in preprocessing text data, specifically converting emails into numeric features suitable for model processing.
+- Set up a supervised learning problem and analyze the results.
+- Understand and follow a typical end-to-end supervised machine learning workflow.
+- Work with a large, real text dataset.
+
+## Dataset
+
+I used the Enron spam email dataset for this project. You can download the dataset using the following links:
+
+- [Enron Emails Dataset - Dev Set](https://cs.colby.edu/courses/S23/cs251/projects/p6spam/data/enron_dev.zip)
+- [Enron Emails Dataset](https://cs.colby.edu/courses/S23/cs251/projects/p6spam/data/enron.zip)
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