GoLorry Data Scientist Interview Questions and Answers

Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Basic Recommendation System Questions
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Q2. A B Testing Questions
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Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts

Q1. Questions related to projects done at my previous company
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Q1. Questions related to my work at previous company
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Q2. ML system design use case type of discussion
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Python test to check basic understanding of algo and classs

Q1. Discussion around project and technical round on ML
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Q1. Basic culture check question and salary discussion expectation.
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Q1. Working of encoders and decoders
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Encoders and decoders are used to convert data from one format to another, such as encoding text into binary or decoding encrypted messages.
• Encoders convert data from one format to another, such as text to binary.

• Decoders reverse the process, converting encoded data back to its original format.

• Examples include Base64 encoding for email attachments and encryption algorithms like AES for secure communication.
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Q2. Where 1D CNN are used
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1D CNNs are used in signal processing, time series analysis, speech recognition, and natural language processing.
• Signal processing: analyzing signals such as audio, EEG, ECG

• Time series analysis: forecasting stock prices, weather patterns

• Speech recognition: converting spoken language to text

• Natural language processing: sentiment analysis, text classification
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Q3. How boosting algorithms works
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Boosting algorithms work by combining multiple weak learners to create a strong learner.
• Boosting algorithms train multiple weak learners sequentially, with each subsequent learner focusing on the mistakes made by the previous ones.

• The final prediction is made by combining the predictions of all the weak learners, usually weighted based on their individual performance.

• Examples of boosting algorithms include AdaBoost, Gr
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Q4. Benefits of using 1×1 kernel in Cnn
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1x1 kernels in CNN help in reducing the number of parameters and computational cost while increasing the non-linearity of the network.
• 1x1 kernels are used to perform dimensionality reduction by combining features from different channels.

• They help in increasing the non-linearity of the network by introducing additional non-linearities through activation functions.

• 1x1 convolutions are computationally efficient compared t...read more
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Q5. What is Auc and what does it indicates
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AUC stands for Area Under the Curve and indicates the performance of a classification model.
• AUC is a metric used to evaluate the performance of a classification model.

• It measures the ability of the model to distinguish between positive and negative classes.

• AUC ranges from 0 to 1, where a higher value indicates better performance.

• An AUC of 0.5 suggests the model is no better than random guessing, while an AUC of 1 indic
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Q1. Explain projects done in details
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Q2. A guesstimate of no of flights on Bengaluru airport
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Case study design oka6y

Q1. Case study explain
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Q2. Ml concept advance
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