Welcome to my repository machine learning internship at CODSOFT! Here, I embark on an exciting journey exploring various aspects of machine learning, from classic classification tasks to addressing real-world challenges. This repository serves as a portfolio to showcase my progress and learnings.
In this project, I'm diving into the world of natural language processing to predict the genre of a movie based on its plot summary. Leveraging techniques like TF-IDF and word embeddings, I'll experiment with classifiers such as Naive Bayes, Logistic Regression, and Support Vector Machines. The goal is to unravel patterns in textual information and enhance my understanding of text-based classification challenges.
In this impactful project, I'm focusing on building a robust model to detect fraudulent credit card transactions. By exploring algorithms like Logistic Regression, Decision Trees, and Random Forests, I aim to enhance my skills in anomaly detection. This project provides a practical understanding of how machine learning can contribute to real-world issues, particularly in ensuring financial security.
Fraudulent SMS messages often attempt to deceive users into revealing sensitive information or engaging in malicious activities. Traditional rule-based methods may not effectively capture the dynamic nature of fraudulent content.
In this project, the goal is to develop a model for detecting fraudulent SMS messages using the TF-IDF (Term Frequency-Inverse Document Frequency) technique and the Naive Bayes algorithm. Fraudulent SMS messages can pose significant security risks and negatively impact users' experiences. By leveraging natural language processing and machine learning, this project aims to identify and filter out fraudulent messages, enhancing the overall security of communication systems.
This one-month internship at CODSOFT provides an immersive experience in machine learning, allowing me to delve into diverse projects and gain hands-on expertise. The internship encompasses a blend of theoretical learning, practical implementation, and exposure to real-world datasets, offering a holistic understanding of machine learning in various applications.
Throughout this journey, I aim to enhance my problem-solving skills, deepen my understanding of machine learning algorithms, and develop a knack for translating data into actionable insights. I am excited to contribute to the field and apply my learnings to solve practical challenges.
Feel free to explore the projects, provide feedback, or connect with me if you share a passion for machine learning and data science. Thank you for joining me on this learning adventure!