1.### What is

### What is *K-Nearest Neighbors (K-NN)* in the context of *machine learning*?

Answer:

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Only coding challenges

- 1.
### What is

*K-Nearest Neighbors (K-NN)*in the context of*machine learning*?Answer: - 2.
### How does the

*K-NN algorithm*work for*classification*problems?Answer: - 3.
### Explain how

*K-NN*can be used for*regression*.Answer: - 4.
### What does the ‘K’ in

*K-NN*stand for, and how do you choose its value?Answer: - 5.
### List the

*pros and cons*of using the*K-NN algorithm*.Answer: - 6.
### In what kind of situations is

*K-NN*not an ideal choice?Answer: - 7.
### How does the choice of

*distance metric*affect the*K-NN algorithm’s performance*?Answer: - 8.
### What are the effects of

*feature scaling*on the*K-NN algorithm*?Answer:

- 9.
### How does

*K-NN*handle*multi-class*problems?Answer: - 10.
### Can

*K-NN*be used for*feature selection*? If yes, explain how.Answer: - 11.
### What are the differences between

*weighted K-NN*and*standard K-NN*?Answer: - 12.
### How does the

*curse of dimensionality*affect*K-NN*, and how can it be*mitigated*?Answer: - 13.
### Discuss the impact of

*imbalanced datasets*on the*K-NN algorithm*.Answer: - 14.
### How would you explain the concept of

*locality-sensitive hashing*and its relation to*K-NN*?Answer: - 15.
### Explore the differences between

*K-NN*and*Radius Neighbors*.Answer:

- 16.
### If you have a

*large dataset*, how can you make*K-NN’s computation*faster?Answer: - 17.
### How do you handle

*categorical features*when implementing*K-NN*?Answer: - 18.
### What is the role of

*data normalization*in*K-NN*, and how is it performed?Answer: - 19.
### How do you assess the

*similarity*between instances in*K-NN*?Answer: - 20.
### Describe the process of

*cross-validation*in the context of tuning*K-NN’s hyperparameters*.Answer: - 21.
### Discuss how

*missing values*in the dataset affect*K-NN*and how you would handle them.Answer: - 22.
### Outline strategies you would use to select an appropriate

*distance metric*for*K-NN*.Answer: - 23.
### What is a

*kd-tree*, and how can it be used to optimize*K-NN*?Answer:

- 24.
### Compare

*K-NN*to*decision trees*. What are the key differences in their algorithms?Answer: - 25.
### How can

*ensemble methods*be used in conjunction with*K-NN*?Answer: - 26.
### Explore the use of

*K-NN*for*outlier detection*and the rationale behind it.Answer: - 27.
### Explain how

*K-NN*can be adapted for*time-series prediction*.Answer: - 28.
### Compare and contrast the use of

*K-NN*in a supervised context versus its use in*unsupervised learning*(e.g., clustering).Answer: - 29.
### Discuss how

*bootstrap aggregating (bagging)*can improve the performance of*K-NN*.Answer:

- 30.
### Write a

*Python function*to implement*K-NN from scratch*on a simple dataset.Answer: - 31.
### Use

*scikit-learn*to demonstrate*K-NN classification*using the*Iris dataset*.Answer: - 32.
### Implement a

*LazyLearningClassifier*in*Python*that uses*K-NN*under the hood.Answer: - 33.
### Create a

*Python script*to visualize the*decision boundary*of*K-NN*on a*2D dataset*.Answer: - 34.
### Develop a

*weighted K-NN algorithm*in*Python*and test its performance against the standard*K-NN*.Answer: - 35.
### Optimize a

*K-NN model*in a large dataset using*approximate nearest neighbors*techniques like*LSH*or*kd-trees*.Answer:

- 36.
### Given a dataset with

*time-series data*, how would you apply*K-NN*for forecasting?Answer: - 37.
### Describe a scenario where you would use

*K-NN*for*image classification*.Answer: - 38.
### How would you apply the

*K-NN algorithm*in a*recommendation system*?Answer: - 39.
### Discuss a healthcare application where

*K-NN*could be beneficial. How would you implement it?Answer: - 40.
### In a retail context, explain how

*K-NN*could be used for*customer segmentation*.Answer:

- 41.
### Summarize the main ideas of a few recent research papers on improving the

*K-NN algorithm*.Answer: - 42.
### Explain how the

*K-NN algorithm*can be*parallelized*. What are the challenges and benefits?Answer: - 43.
### How can

*K-NN*be combined with*deep learning techniques*?Answer: - 44.
### Discuss the role of

*approximate nearest neighbor search*in scaling*K-NN*for*big data*.Answer: - 45.
### What are the trends and future advancements in the field of

*K-NN*and its applications?Answer:

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