Date: 21 June 2022 – Click here to see LinkedIn Post.

Q1. How will you handle heavy data?

A. Follow these techniques
1. Allocate more memory
2. Work with a smaller sample
3. Use a relational database like SQL
4. Use a big data platform such as Hadoop.

Q2. Explain overfitting and what steps you can take to prevent it.

A. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers/number of units per layer
4. Cross-validation

Q3. Describe the decision tree model.

A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets. 

Q4. Why do you use feature selection?

A. The aim of feature selection is to maximize relevance and minimize redundancy. Feature selection methods can be used in data pre-processing to achieve efficient data reduction. This is useful for finding accurate data models.

Q5. How do you pick k for K-Means?

A. There is a popular method known as the elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost by changing k. As the value of K increases, there will be fewer elements in the cluster.

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