Welcome to the Codsoft repository, where I present the Machine Learning projects I developed during my internship with CodSoft from October 5 to November 5, 2024. Each project focuses on applying machine learning techniques to real-world tasks, such as text classification, fraud detection, and customer behavior analysis.
In this project, I built a model to predict a movie's genre based on its plot summary. Techniques such as TF-IDF and word embeddings were applied to transform text data, and various classifiers, including Naive Bayes, Logistic Regression, and Support Vector Machines, were experimented with to determine the most accurate approach.
This project aims to identify fraudulent credit card transactions. Using a dataset with transaction details, I developed a model that applies algorithms like Logistic Regression, Decision Trees, and Random Forests to classify transactions as legitimate or fraudulent. Emphasis was placed on achieving a balance between precision and recall.
The goal of this project is to predict customer churn in a subscription-based service. By analyzing customer demographics and behavior, I applied algorithms such as Logistic Regression, Random Forests, and Gradient Boosting to build a predictive model that identifies customers at risk of churning.
In this project, I created a model to classify SMS messages as spam or legitimate. I used text preprocessing methods, such as TF-IDF and word embeddings, and classifiers like Naive Bayes, Logistic Regression, and Support Vector Machines to develop an effective spam filter.
This repository demonstrates hands-on implementations of machine learning principles, covering data preprocessing, feature extraction, and model evaluation. Each project includes a Jupyter Notebook detailing the data pipeline, exploratory analysis, model selection, and evaluation metrics, making this a valuable reference for both learning and application in machine learning.