Voya India Data Scientist Interview Questions, Process, and Tips

Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor generalization to new data.

• Overfitting happens when a model learns the noise in the training data instead of the underlying pattern.

• It occurs when the model is too complex or has too many parameters relative to the amount of training data.

• Overfitting can be identified by comparing the model's performance on the trai...read more

Handling missing data involves identifying missing values, deciding on a strategy, and implementing it.

• Identify missing data using summary statistics or visualization techniques.

• Decide on a strategy: imputation, deletion, or modeling.

• Imputation: replace missing values with mean, median, mode, or regression predictions.

• Deletion: remove rows or columns with missing values.

• Modeling: treat missingness as a separate categor...read more

The time complexity of KNN algorithm is O(n log n) for training and O(kn) for testing.

• The time complexity for training the KNN algorithm is O(n log n), where n is the number of training samples.

• The time complexity for testing the KNN algorithm is O(kn), where k is the number of nearest neighbors to consider and n is the number of training samples.

• The time complexity can be further optimized using data structures like K

Use dimensionality reduction techniques like PCA or t-SNE to reduce the number of features and plot the scatter plot.

• Apply Principal Component Analysis (PCA) to reduce the dimensionality of the data

• Use t-Distributed Stochastic Neighbor Embedding (t-SNE) for non-linear dimensionality reduction

• Plot the scatter plot using the reduced feature set

Yes, random forest can be used for text data with important features.

• Random forest can handle both numerical and categorical features, including text data.

• Text data needs to be converted into numerical features using techniques like bag-of-words or TF-IDF.

• Important features can be identified using feature importance scores provided by random forest.

• Examples: Classifying emails as spam or not spam, sentiment analysis of