Accenture Data Scientist Interview Questions, Process, and Tips

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more

Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.

• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')

Developed a predictive model to forecast customer churn in a telecom company

• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to the company to reduce customer churn rate

I played a key role in designing the data collection process, analyzing the data, and developing predictive models.

• Led the team in defining project goals and objectives

• Designed and implemented data collection methods

• Analyzed data using statistical techniques and machine learning algorithms

• Developed predictive models to support decision-making

• Collaborated with stakeholders to interpret results and make recommendations

Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.

• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization to new, unseen data.

• Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

• Example: A decision tree with too many branches that perfectly fits the training d

CNN is used for image recognition, RNN is used for sequential data like text or time series.

• CNN is Convolutional Neural Network, used for image recognition tasks.

• RNN is Recurrent Neural Network, used for sequential data like text or time series.

• CNN uses convolutional layers to extract features from images, while RNN uses recurrent connections to remember past information.

• CNN is good at capturing spatial dependencies in...read more