DataSift

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  Naive Bayes

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  Logistic Regression

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  Train, Validation and Test

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  KNN

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  Data

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  Decision Tree

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  Random Forest

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  Bagging

Objective:

This project aims to determine the most suitable classification method for a given dataset. The selected classification algorithms for evaluation include:

K-Nearest Neighbors (KNN)

Naive Bayes

Logistic Regression

Support Vector Machines (SVM)

Methodology:

Dataset: Utilize a representative dataset for the classification task. Dataset Source: /www.kaggle.com/datasets/michau96/classification-syntetic-data-for-practice/

Algorithms:

Implement KNN for classification. Apply Naive Bayes algorithm. Employ Logistic Regression. Utilize Support Vector Machines (SVM).

Evaluation:

Compare the predictions of each algorithm with the actual (ground truth) values. Measure and analyze the performance metrics for each method.

Outcome:

Identify the classification algorithm that demonstrates the highest accuracy and reliability. Present a comparative analysis of the performance of KNN, Naive Bayes, Logistic Regression, and SVM.

Built With

• data
• imblearn
• keras
• numpy
• pandas
• python
• sklearn
• tensorflow