{"id":24685039,"url":"https://github.com/dipeshdimi/credit_card_fraud_detection","last_synced_at":"2026-05-14T23:02:30.041Z","repository":{"id":220052164,"uuid":"750622328","full_name":"dipeshdimi/Credit_Card_Fraud_Detection","owner":"dipeshdimi","description":null,"archived":false,"fork":false,"pushed_at":"2024-02-03T02:02:01.000Z","size":29,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-21T20:46:37.092Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dipeshdimi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-01-31T01:39:28.000Z","updated_at":"2024-01-31T02:45:11.000Z","dependencies_parsed_at":"2024-01-31T03:46:47.574Z","dependency_job_id":"e4429350-a598-4e7e-b170-1414b0a682bd","html_url":"https://github.com/dipeshdimi/Credit_Card_Fraud_Detection","commit_stats":null,"previous_names":["dipeshdimi/credit_card_fraud_detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dipeshdimi/Credit_Card_Fraud_Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshdimi%2FCredit_Card_Fraud_Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshdimi%2FCredit_Card_Fraud_Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshdimi%2FCredit_Card_Fraud_Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshdimi%2FCredit_Card_Fraud_Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dipeshdimi","download_url":"https://codeload.github.com/dipeshdimi/Credit_Card_Fraud_Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshdimi%2FCredit_Card_Fraud_Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278938376,"owners_count":26072271,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-26T15:17:38.089Z","updated_at":"2025-10-08T11:45:35.289Z","avatar_url":"https://github.com/dipeshdimi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Credit Card Fraud Detection\n\n## Introduction\nThis repository contains a Jupyter Notebook (`Credit_Card_Fraud_Detection.ipynb`) that focuses on detecting fraudulent credit card transactions using logistic regression. The notebook includes steps for data loading, exploration, preprocessing, model training, and evaluation.\n- [Colab Link](https://colab.research.google.com/drive/1B1IJKPkXi4PAGA9NjJeBSmEQlck9u55-?usp=sharing)\n\n## Dataset\nThe dataset used in this project can be found on [Credit Card Fraud Dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud). It consists of transactions labeled as legitimate (Class 0) or fraudulent (Class 1). Please note that the dataset is relatively large, so the initial loading may take some time.\n\n## Dependencies\nTo run the notebook, make sure you have the following dependencies installed:\n```python\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix, f1_score, accuracy_score, precision_score, recall_score\n```\n## Exploratory Data Analysis\nThe notebook includes an exploration of the dataset, covering aspects such as data overview, information, checking for missing values, and analyzing the class distribution.\n\n## Data Balancing\nGiven the highly unbalanced nature of the dataset, with a significant number of legitimate transactions (Class 0) and fewer fraudulent transactions (Class 1), the notebook implements under-sampling to balance the dataset for training purposes.\n\n## Model Training\nLogistic Regression is chosen as the classification algorithm for this task. The notebook includes code for training the logistic regression model using the balanced dataset.\n\n## Model Evaluation\nThe notebook evaluates the trained model on both the training and testing datasets, providing metrics such as accuracy, confusion matrix, precision, recall, and F1 score.\n\n## Results\nAfter training and evaluating the logistic regression model, the notebook displays the performance metrics on both the training and testing datasets.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshdimi%2Fcredit_card_fraud_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdipeshdimi%2Fcredit_card_fraud_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshdimi%2Fcredit_card_fraud_detection/lists"}