Project Overview: This project focuses on developing a spam email classifier using advanced machine learning techniques. The goal is to accurately identify spam emails and improve email filtering efficiency.
Steps Involved-
Data Loading and Exploration: Load the dataset and explore its structure. Understand the columns, features, and labels. Familiarize yourself with the dataset's format.
Data Preprocessing: Handle missing values and prepare the data for training. Impute missing values using mean values for each feature.
Model Building: Implement a machine learning model using the Multinomial Naive Bayes algorithm for text classification. Train the model using the preprocessed data.
Model Evaluation: Evaluate the trained model using metrics such as accuracy, precision, recall, and F1-score. Understand the model's performance in classifying emails as spam or not spam.
Interpretation of Results: Interpret the results obtained, focusing on the accuracy score. An accuracy of 0.95 indicates the model's effectiveness in distinguishing between spam and non-spam emails.
Author: Viraj N. Bhutada
Machine Learning Algorithm Used: Multinomial Naive Bayes
Description:
In this project, I developed a spam email classifier using the Multinomial Naive Bayes algorithm. By leveraging advanced machine learning techniques, the model achieved an accuracy score of 0.95, demonstrating its proficiency in distinguishing between spam and non-spam emails. The steps involved in the project include data loading, preprocessing, model building, evaluation, and result interpretation. This project showcases the effective application of machine learning in solving real-world email filtering challenges.
Dataset: https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv
This project focuses on developing a credit card fraud detection system using advanced machine learning techniques. The primary objective is to accurately identify fraudulent credit card transactions, ensuring financial security and preventing monetary losses for individuals and businesses.
Load the credit card fraud dataset and explore its structure. Understand the dataset's features, including columns, and labels. Familiarize yourself with the dataset's format and contents.
Standardize features, handle missing values, and split the data for training and testing. Impute missing values and ensure data consistency for effective modeling.
Visualize data distribution and explore correlations among features. Utilize techniques like histograms and heatmaps for comprehensive data analysis.
Utilize the Random Forest Classifier algorithm for accurate fraud detection. Train the model using the preprocessed data to enable reliable identification of fraudulent transactions.
Assess model accuracy, precision, recall, and F1-score. Evaluate the model's performance in classifying transactions as fraudulent or legitimate.
Author: Viraj N. Bhutada
Machine Learning Algorithm Used: Random Forest Classifier
In this project, a credit card fraud detection system was developed using the Random Forest Classifier algorithm. Leveraging advanced machine learning techniques, the model achieved an impressive accuracy score of 0.99, demonstrating its proficiency in accurately identifying fraudulent transactions. The project involved key steps such as data loading, preprocessing, exploratory data analysis, model training, and evaluation. The successful outcome underscores the effective application of machine learning in ensuring financial security and integrity in digital transactions.