{"id":21458214,"url":"https://github.com/farooqueesamiya/social-network-ads-classification-models","last_synced_at":"2026-01-03T22:14:42.492Z","repository":{"id":188958299,"uuid":"679767999","full_name":"farooqueesamiya/Social-Network-Ads-Classification-Models","owner":"farooqueesamiya","description":"In this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.","archived":false,"fork":false,"pushed_at":"2023-08-17T15:18:29.000Z","size":52,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-23T13:43:57.404Z","etag":null,"topics":["classification-model","decision-tree-classifier","knn-classifier","linear-kernel","logistic-regression","naive-bayes-classifier","random-forest-classifier","rbf-kernel","support-vector-machine","svc-model"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/code/samiyafarooquee/classification-models","language":"Python","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/farooqueesamiya.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":"2023-08-17T15:14:37.000Z","updated_at":"2024-11-20T15:58:07.000Z","dependencies_parsed_at":null,"dependency_job_id":"425404eb-1939-4e35-8984-7e417368ac70","html_url":"https://github.com/farooqueesamiya/Social-Network-Ads-Classification-Models","commit_stats":null,"previous_names":["farooqueesamiya/social-network-ads-classification-models"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farooqueesamiya%2FSocial-Network-Ads-Classification-Models","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farooqueesamiya%2FSocial-Network-Ads-Classification-Models/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farooqueesamiya%2FSocial-Network-Ads-Classification-Models/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farooqueesamiya%2FSocial-Network-Ads-Classification-Models/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/farooqueesamiya","download_url":"https://codeload.github.com/farooqueesamiya/Social-Network-Ads-Classification-Models/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243971212,"owners_count":20376784,"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","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":["classification-model","decision-tree-classifier","knn-classifier","linear-kernel","logistic-regression","naive-bayes-classifier","random-forest-classifier","rbf-kernel","support-vector-machine","svc-model"],"created_at":"2024-11-23T06:18:36.419Z","updated_at":"2026-01-03T22:14:37.468Z","avatar_url":"https://github.com/farooqueesamiya.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Social-Network-Ads-Classification-Models\nIn this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.\n\n# Target Audience Prediction\n\n# Introduction\nThis document provides an overview of a classifier comparison and decision boundary\nvisualization using various machine learning classifiers. The classifiers are evaluated on the\n\"Social_Network_Ads\" dataset, aiming to predict whether a user purchased a product based on\ntheir age and estimated salary.\n\n# Dataset\nThe dataset, \"Social_Network_Ads.csv,\" contains information about users' age, estimated\nsalary, and purchase decision. It is loaded and preprocessed for analysis.\n\n# Classifiers\nThe following classifiers are used for prediction and comparison:\nDecision Tree Classifier (Entropy-based)\nSupport Vector Classifier (SVC) with Radial Basis Function (RBF) Kernel\nGaussian Naive Bayes Classifier\nRandom Forest Classifier\nSupport Vector Classifier (SVC) with Linear Kernel\nk-Nearest Neighbors (KNN) Classifier\nLogistic Regression\n\n# Workflow\nData Preparation: The dataset is loaded, and the features (age and estimated salary) are\nextracted, along with the target variable (purchase decision).\nData Splitting and Standardization: The dataset is split into training and testing sets using a\n75-25 split ratio. The features are standardized using the StandardScaler to ensure consistent\nscaling for model training.\nClassifier Comparison: Each classifier is trained on the training data and evaluated on the\ntesting data. Accuracy scores and confusion matrices are calculated to assess classifier\nperformance.\nDecision Boundary Visualization: For each classifier, the decision boundary is visualized on the\ntest set. Age and estimated salary are used as the x and y axes, respectively. Points are colored\naccording to their true class label, providing insights into how well the classifier separates the\nclasses.\n\n# Results\nThe performance of each classifier is evaluated based on accuracy and the confusion matrix:\n\n# Conclusion\nThe classifier comparison and decision boundary visualization provide insights into the\nperformance of different machine learning classifiers on the \"Social_Network_Ads\" dataset. The\nSupport Vector Classifier (SVC) with RBF Kernel and k-Nearest Neighbors (KNN) Classifier\nachieved the highest accuracy (0.93) in predicting whether a user purchased a product. The\ndecision boundary visualizations enhance our understanding of how these classifiers separate\nthe classes based on age and estimated salary. This analysis can guide the selection of an\nappropriate classifier for similar prediction tasks.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarooqueesamiya%2Fsocial-network-ads-classification-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarooqueesamiya%2Fsocial-network-ads-classification-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarooqueesamiya%2Fsocial-network-ads-classification-models/lists"}