https://github.com/farooqueesamiya/social-network-ads-classification-models
In this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.
https://github.com/farooqueesamiya/social-network-ads-classification-models
classification-model decision-tree-classifier knn-classifier linear-kernel logistic-regression naive-bayes-classifier random-forest-classifier rbf-kernel support-vector-machine svc-model
Last synced: 3 months ago
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In this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.
- Host: GitHub
- URL: https://github.com/farooqueesamiya/social-network-ads-classification-models
- Owner: farooqueesamiya
- Created: 2023-08-17T15:14:37.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-17T15:18:29.000Z (almost 2 years ago)
- Last Synced: 2025-01-23T13:43:57.404Z (5 months ago)
- 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
- Language: Python
- Homepage: https://www.kaggle.com/code/samiyafarooquee/classification-models
- Size: 50.8 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Social-Network-Ads-Classification-Models
In this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.
# Target Audience Prediction
# Introduction
This document provides an overview of a classifier comparison and decision boundary
visualization using various machine learning classifiers. The classifiers are evaluated on the
"Social_Network_Ads" dataset, aiming to predict whether a user purchased a product based on
their age and estimated salary.
# Dataset
The dataset, "Social_Network_Ads.csv," contains information about users' age, estimated
salary, and purchase decision. It is loaded and preprocessed for analysis.
# Classifiers
The following classifiers are used for prediction and comparison:
Decision Tree Classifier (Entropy-based)
Support Vector Classifier (SVC) with Radial Basis Function (RBF) Kernel
Gaussian Naive Bayes Classifier
Random Forest Classifier
Support Vector Classifier (SVC) with Linear Kernel
k-Nearest Neighbors (KNN) Classifier
Logistic Regression
# Workflow
Data Preparation: The dataset is loaded, and the features (age and estimated salary) are
extracted, along with the target variable (purchase decision).
Data Splitting and Standardization: The dataset is split into training and testing sets using a
75-25 split ratio. The features are standardized using the StandardScaler to ensure consistent
scaling for model training.
Classifier Comparison: Each classifier is trained on the training data and evaluated on the
testing data. Accuracy scores and confusion matrices are calculated to assess classifier
performance.
Decision Boundary Visualization: For each classifier, the decision boundary is visualized on the
test set. Age and estimated salary are used as the x and y axes, respectively. Points are colored
according to their true class label, providing insights into how well the classifier separates the
classes.
# Results
The performance of each classifier is evaluated based on accuracy and the confusion matrix:
# Conclusion
The classifier comparison and decision boundary visualization provide insights into the
performance of different machine learning classifiers on the "Social_Network_Ads" dataset. The
Support Vector Classifier (SVC) with RBF Kernel and k-Nearest Neighbors (KNN) Classifier
achieved the highest accuracy (0.93) in predicting whether a user purchased a product. The
decision boundary visualizations enhance our understanding of how these classifiers separate
the classes based on age and estimated salary. This analysis can guide the selection of an
appropriate classifier for similar prediction tasks.