CLASSIFY WISCONSIN BREAST CANCER DATASET

I'm looking for a data scientist who can implement the k-Nearest Neighbors (kNN) algorithm, Decision Trees, and Random Forest to classify the Wisconsin Breast Cancer dataset.
Key Requirements.
- Utilize all available features in the dataset for model training.
- Output classification results in an Excel file.
- Provide a detailed output including the confusion matrix for model evaluation.
Ideal Skills.
- Proficiency in Python or R for implementing machine learning algorithms.
- Experience with cancer datasets is a plus.
- Strong understanding of model evaluation metrics.
Please use the original version of the Wisconsin Breast Cancer dataset.
Please use Python with scikit-learn for implementing the algorithms.
No feature engineering or data preprocessing is needed; use the dataset as is.
Include visualizations of the data and results.
Use the default parameters for the kNN, Decision Trees, and Random Forest algorithms.
Present data and results as static images.
Please include graphs and charts for the data and results visualizations.
Include an ROC curve for each model in the visualizations.
Please provide a detailed comparison of the models' performances.
The final presentation of findings should be in the form of PowerPoint slides.
Please detail procedure.
Python Machine Learning (ML) Extração de Dados Linguagem de Programação R Ciência de Dados ID do Projeto.
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