Email Spam detection using Python
Email spam detection is a common and important project in the field of Natural Language Processing (NLP) and Machine Learning.
Email Spam Detection Using Python
Email spam detection can be implemented using machine learning techniques in Python. Below is a step-by-step guide to building an email spam detection system using a popular dataset and a Naive Bayes classifier as an example.
Step 1: Import Necessary Libraries
Step 2: Load and Prepare the Dataset
You can use a dataset like the Spam.csv dataset. Make sure you have the dataset downloaded and organized. Then, load it into your Python environment.
Step 3: Text Preprocessing
Preprocess the email text data by tokenizing, converting to lowercase, and removing punctuation.
Step 4: Split the Dataset
Split the dataset into training and testing sets.
Step 5: Feature Extraction
Convert the text data into numerical features using a technique like Count Vectorization.
Step 6: Train a Classifier
Train a Naive Bayes classifier on the training data.
Step 7: Make Predictions and Evaluate
Make predictions on the test data and evaluate the model's performance.
Submitted by Bommala Shreya (bommalashreya09)
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