GeoIQ Interview Questions and Answers

Q1. Explain your approach if you were given this problem statement: "Timeseries forecasting model has to be trained for predicting quantity of medicines required on 500 medical stores associated with TATA 1mg ...read more
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Q2. Follow-up questions were there based on the approach which I explained for Question 1 which includes data collection and data preparation steps involved, choosing the right model for training, how to divid...read more
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Q3. How underfitting and overfitting can be avoided when dealing with Machine Learning models?
Ans. 
Avoiding underfitting and overfitting involves techniques like regularization, cross-validation, and proper model selection.
• Use cross-validation to assess model performance on unseen data, helping to identify overfitting.

• Implement regularization techniques (L1, L2) to penalize overly complex models.

• Choose the right model complexity; simpler models can prevent overfitting.

• Increase training data to provide more examples ...read more
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Q4. Can we use linear regression model for given problem considering till today's data as input to the model and forecasting for tomorrow?
Ans. 
Linear regression can be used for forecasting, but its effectiveness depends on data patterns and relationships.
• Linear regression assumes a linear relationship between input and output variables.

• If the data shows a consistent trend, linear regression can provide reasonable forecasts.

• Example: Predicting tomorrow's temperature based on historical temperature data.

• However, it may not capture complex patterns or seasonalit...read more
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Q5. Coding question on rotation of matrix 'n' times by 90 degrees.
Ans. 
Rotate a square matrix 90 degrees clockwise 'n' times efficiently.
• A 90-degree clockwise rotation can be achieved by transposing the matrix and then reversing each row.

• Example: For matrix [[1, 2], [3, 4]], after one rotation it becomes [[3, 1], [4, 2]].

• To rotate 'n' times, calculate effective rotations as n % 4 since every 4 rotations return the matrix to its original state.

• If n = 1, rotate once; if n = 2, rotate twice ...read more
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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. Questions on Past Project
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Q2. SQL Dense Rank - also having the option to do in Pandas
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Q3. Pandas groupby on a dataset given - required to calculate group wise yoy rate of a column
Ans. 
Calculate year-over-year (YoY) rates using Pandas groupby for a specific column in a dataset.
• Use the Pandas 'groupby' method to group data by the desired category (e.g., 'year').

• Apply the 'pct_change()' function to calculate the percentage change between years.

• Example: df.groupby('year')['sales'].sum().pct_change() to get YoY sales growth.

• Ensure the data is sorted by year before applying the calculation for accurate re...read more
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Q1. Past project ( which he chose , he chose my very first project , which I had forgotten) so ended up screwing it
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Q2. SQL / Pyspark question - difficult question
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Case study design oka6y

Q1. Case study explain
Ans. 
A case study involves analyzing a specific situation to derive insights and solutions using data science techniques.
• Define the problem clearly: Identify the key issues to address.

• Collect relevant data: Gather data from various sources, ensuring quality and relevance.

• Analyze the data: Use statistical methods and machine learning to uncover patterns.

• Draw insights: Interpret the results to provide actionable recommendatio...read more
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Q2. Ml concept advance
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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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Easy topics arrays, sequence sum.