Orient Cement Interview Questions and Answers

It is difficult to estimate the exact number of sofas sold in a day in a city without specific data.

• The number of sofas sold in a day can vary greatly depending on factors such as population size, economic conditions, and consumer preferences.

• One way to estimate could be to look at the number of furniture stores in the city and their average daily sales.

• Another approach could be to conduct a survey of a sample of furniture stores to get an idea of their daily sofa sales.

• Onlin...read more

I approach an end to end ML problem by understanding the problem, collecting data, preprocessing data, selecting a model, training the model, evaluating the model, and deploying the model.

• Understand the problem and define the objective

• Collect and preprocess data

• Select a suitable machine learning model

• Train the model using the data

• Evaluate the model's performance

• Deploy the model for production use

Boosting and bagging are ensemble learning techniques used to improve the performance of machine learning models.

• Boosting focuses on improving the performance of a single model by training multiple models sequentially, where each subsequent model corrects the errors of its predecessor.

• Bagging, on the other hand, involves training multiple models independently and then combining their predictions through averaging or voting.

• Boosting typically results in lower bias and higher v...read more

Evaluation techniques for machine learning algorithms include cross-validation, confusion matrix, ROC curve, and precision-recall curve.

• Cross-validation: Splitting the data into multiple subsets for training and testing to assess model performance.

• Confusion matrix: A table showing the true positive, true negative, false positive, and false negative predictions of a model.

• ROC curve: Receiver Operating Characteristic curve plots the true positive rate against the false positive...read more

Design a demand planning system for efficient forecasting and inventory management.

• Utilize historical sales data to identify trends and seasonality

• Incorporate external factors like market trends, promotions, and competitor activities

• Implement machine learning algorithms for accurate demand forecasting

• Integrate with inventory management systems for optimized stock levels

• Regularly review and adjust the system based on performance metrics