Classification with K-Nearest Neighbors
The k-nearest neighbor (kNN) is used for pattern classification, regression models, and is ideal for data mining. Some real-world examples of its use include determining credit card ratings, identifying who’s likely to default on a loan, detecting unusual patterns in credit card usage, or predicting the future value of stocks.
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Step 1: To perform a classification using the k-nearest neighbors algorithm, complete the following:
- Access https://archive.ics.uci.edu/datasets?Task=Classification&NumInstances=1000-inf&NumAttributes=10-100&skip=0&take=10&sort=desc&orderBy=NumHits&search=
Note: There are about 120 datasets that are suitable for use in a classification task. For this part of the exercise, you must choose one of these datasets, provided it includes at least 10 attributes and 10,000 instances.
- Discuss the origin of the data and assess whether it was obtained in an ethical manner.
Step 2: For your selected dataset, build a classification model as follows, this can be done in either RStudio or Python:
- Explain the dataset and the type of information you wish to gain by applying a classification method.
- Explain the k-nearest neighbors algorithm and how you will be using it in your analysis (list the steps, the intuition behind the mathematical representation, and address its assumptions). Assume k = 5 using the Euclidian distance. Explain the value of k.
- Import the necessary libraries, then read the dataset into a data frame and perform initial statistical exploration.
- Clean the data and address unusual phenomena (e.g., normalization, outliers, missing data, encoding); use illustrative diagrams and plots and explain them.
- Formulate two questions that can be answered by applying a classification method using the k-nearest neighbors method.
- Split the data into 80% training and 20% testing sets.
- Train the k-nearest neighbors classifier on the training set using the following parameters: k = 5, metric = ‘minkowski’, p = 2.
- Make classification predictions.
- Interpret the results in the context of the questions you asked.
- Validate your model using a confusion matrix, accuracy score, ROC-AUC curves, and k-fold cross validation. Then explain the results.
- Include all mathematical formulas used and graphs representing the final outcomes.
Step 3: Prepare a comprehensive technical report as an RMarkdown document or Jupyter notebook, including all code, code comments, all outputs, plots, and analysis.
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Make sure the project documentation contains:
a) Problem statement
b) Algorithm of the solution
c) Analysis of the findings
d) References
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