ACCELQ’s Post

Growintern Task completion. In this project, I developed a machine learning model to detect fraudulent credit card transactions. Using an imbalanced dataset, I first balanced it using SMOTE (Synthetic Minority Over-sampling Technique) and then applied Logistic Regression to classify transactions as fraudulent or legitimate. Key steps included data preprocessing, feature engineering, and model evaluation using metrics such as accuracy score, confusion matrix, and classification report. This project aimed to enhance the detection accuracy and minimize false positives, ensuring a robust and reliable fraud detection system. GitHub Repository Link: https://lnkd.in/gsbX5D27 Growintern #Growintern

CODSOFT INTERN ✨ TASK 2 - Credit card fraud detection 💳 I'm excited to present my most recent credit card fraud detection project. I created a model that reliably detects fraudulent transactions using advanced machine learning techniques, assisting in protecting financial data and averting losses. #codsoft #codsoftinternship #machinelearning Here's the respective demo video of the project.

Well we go for submission of tasks...!!! 👩💻 Building a model to detect fraudulent credit card transactions was the TASK 2 in the CODSOFT Machine Learning tasks.. 😊 And the process begins.....!!!!! 😎 @CodSoft #CodSoft #MachineLearningInternship

Excited to share my latest project: Credit Card Fraud Detection using Machine Learning! Tackling fraudulent transactions with the power of data. Big thanks to CODTECH IT Solutions for the incredible internship opportunity! 💼 CODTECH IT SOLUTIONS Got it! Here's a more concise summary for your Credit Card Fraud Detection project: Objective: Detect fraudulent credit card transactions. Data: Used transaction records with features like amount, time, and location. Preprocessing: Cleaned and normalized the data. EDA: Analyzed patterns and trends in the data. Modeling: Tested models like Logistic Regression, Random Forest, and Gradient Boosting; chose the best-performing one. Handling Imbalance: Used techniques like SMOTE to address class imbalance. Results: Achieved high accuracy and precision in detecting fraud. Implementation: Discussed real-time integration strategies. Future Work: Suggested enhancements like additional data sources and continuous model tuning.  #MachineLearning #CreditCardFraud #CODTECHITSolutions

🚀 Excited to share my latest project! 🎥 In my CodSoft internship, I’ve delved into the intriguing realm of Credit Card Fraud Detection using machine learning. 🤖💳 🎞️ In this video, I walk you through the entire process, from dataset analysis to building a Isolation Forest. We’ll explore how machine learning techniques can help identify fraudulent credit card transactions, ensuring the security of financial transactions for users. 💡 🔗 GitHub Repository: Desty27/CODSOFT Join me on this journey of data analysis and machine learning, and let’s combat credit card fraud together! 💪🌟 #MachineLearning #FraudDetection #DataScience #GitHub #codsoft

🔒 Credit Card Fraud Detection: Enhancing Financial Security with Machine Learning 🔒 I’m excited to share my fifth project during my internship at #Codsoft! I developed a machine learning model to identify fraudulent credit card transactions. This project aims to enhance financial security by accurately detecting and preventing fraud. 🔍 Project Highlights: 1. Data Preprocessing: Normalized transaction data and addressed class imbalance issues to ensure robust model performance. 2. Dataset Splitting: Split the dataset into training and testing sets for accurate model evaluation. 3. Model Training: Trained classification algorithms such as logistic regression and random forests to classify transactions as fraudulent or genuine. 4. Model Evaluation: Evaluated the model’s performance using precision, recall, and F1-score metrics to ensure reliable fraud detection. 5. Imbalance Handling: Employed techniques like oversampling and undersampling to improve model results and handle class imbalance. This project has been a fantastic opportunity to delve into the world of financial data analysis, feature engineering, and machine learning. By developing effective fraud detection models, we can significantly enhance the security and reliability of financial transactions. Check out my work on GitHub:https://lnkd.in/dgvChSzg #DataScience #MachineLearning #Python #FraudDetection #CreditCardFraud #DataAnalysis #codsoft #Internship #Tech #FinancialSecurity

Encryptix Task 2: Credit Card Fraud Detection I recently completed my first task at Encryptix , focusing on detecting fraudulent transactions in credit card data. Data Preprocessing: - Loaded and explored the Credit Card Fraud Detection Dataset from Kaggle. - Addressed missing values and encoded categorical variables. - Balanced the dataset through upsampling. Feature Engineering: - Applied LabelEncoder to categorical features. - Removed irrelevant columns and split the data into features and target variables. Model Training and Evaluation: - Trained a RandomForestClassifier. - Evaluated the model using accuracy, precision, recall, ROC AUC, and PR AUC metrics. #encryptix #internship #machinelearning

Hey Connections! 🚀💳 Thrilled to wrap up my CodSoft internship journey with a bang - presenting my final project video on Credit Card Fraud Detection using Machine Learning! 🕵️♂️🔍 In a world where digital transactions are skyrocketing, protecting against fraud has never been more crucial. This project puts AI on the frontlines of financial security! 🛡️💻 🎥 In this video, you'll see how I: Dived deep into transaction data, uncovering hidden patterns 🔬 Battled the notorious class imbalance problem head-on 🥊 Trained cutting-edge algorithms like Random Forests and Logistic Regression 🌳🧮 Fine-tuned the model using techniques like oversampling and undersampling 🔧 Evaluated performance with a keen eye on precision, recall, and F1-score 📊 This project has been an incredible learning experience, showing me how data scientists can be the unsung heroes in the fight against financial crime. We're not just crunching numbers; we're safeguarding millions of transactions every day! 💪💰 Want to peek under the hood? My code is waiting for you on GitHub! Here you go 👉👇 https://lnkd.in/eJCDFEXJ 💭 Food for thought: As AI gets smarter, how do you think fraudsters will adapt their tactics? And how can we stay one step ahead? #CodSoft #DataScience #MachineLearning #FraudDetection #FinTech #CybersecurityInFinance #Grateful #Thankyou

🚀 Excited to Share My Latest Achievement at CODTECH IT SOLUTIONS! 🚀 I’m thrilled to announce the successful completion projects as part of my internship at CODTECH IT SOLUTIONS: 2. Credit Card Fraud Detection : I implemented a machine learning model designed to detect fraudulent credit card transactions, which played a crucial role in enhancing financial security measures. This project sharpened my skills in anomaly detection and highlighted the importance of precision in real-time decision-making. #codtechitsolution #machinelearning

Hello LinkedIn family 🤞 , I have successfully completed my 1ST task of "Codsoft" on MACHINE LEARNING Internship. My task is all about to develop the model to predict the 'Credit Card Fraud Detection'. 🔍 Task 1 Completed: Credit Card Fraud Detection 🔍 Exciting news! I've successfully completed Task 1 of our credit card fraud detection project, and I'm thrilled to share our progress with you all! 🎉 In this phase, we focused on data preprocessing and exploration to gain insights into the patterns of fraudulent transactions. Here's a brief overview of what we accomplished: 📊 Data Cleaning and Preprocessing: We cleaned the dataset, handled missing values, and standardized the features to prepare them for analysis. 🔍 Exploratory Data Analysis (EDA): Through statistical analysis, we uncovered interesting trends and correlations within the data. Understanding these patterns is crucial for building effective fraud detection models. 🔢 Feature Engineering: We engineered new features and selected the most relevant ones to enhance the predictive power of our model. This step is essential for capturing the underlying patterns of fraudulent activity. Next up, we'll be diving into the exciting world of machine learning to develop and train our fraud detection model. Stay tuned for updates on Task 2 as we work towards building a robust solution to combat credit card fraud! 💳🛡#codsoft Codesoft #CreditCardFraudDetection #DataScience #MachineLearning #DataPreprocessing #ExploratoryDataAnalysis #FeatureEngineering #TaskCompleted #ProjectUpdate #StayTuned Github:https://lnkd.in/gU6tiu22