This repository contains a machine learning project for email classification. The objective of this project is to develop and evaluate machine learning models to distinguish between spam and legitimate emails based on their content.
The dataset used in this project consists of a collection of emails labeled as either spam or not spam. The dataset is preprocessed to extract relevant features from the email content, such as text, subject, sender, and recipient information.
- Data Collection & Preprocessing: This section includes code for loading and preprocessing the email dataset, handling missing values, and transforming text data into a format suitable for machine learning models.
- Exploratory Data Analysis (EDA): Visualizes the distribution of spam and non-spam emails, explores key features, and analyzes patterns within the dataset.
- Feature Engineering: Extracts relevant features from the email content and performs feature selection to identify the most informative features for classification.
- Model Building: Implements and evaluates various machine learning models, including Logistic Regression, Decision Tree, Naive Bayes, Support Vector Machine, and K-Nearest Neighbors.
- Model Evaluation: Compares the performance of different models using metrics such as accuracy, precision, recall, and F1-score.
- Visualization: Presents visualizations of model performance and key insights derived from the analysis.
To run the project on your local machine, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies.
- Execute the Jupyter Notebook or Python scripts in the specified order to perform data preprocessing, model training, and evaluation.
The project results demonstrate that the best-performing model achieves the highest accuracy. Detailed performance metrics for each model are provided in the project documentation.
- Muhammad Ubaidullah