GeoIQ 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. A SQL question that involves window functions
Ans. 
SQL window functions allow for advanced data analysis by performing calculations across a set of table rows related to the current row.
• Window functions operate on a set of rows defined by an OVER() clause.

• Common window functions include ROW_NUMBER(), RANK(), and SUM().

• Example: SELECT employee_id, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank FROM employees;

• Window functions can be used for running totals, mo...read more
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Q2. Python coding round that involved string manipulation
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Simple mediam DSA style questions around ML

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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Q1. ML System design
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Q2. CNN networks why are they better then NN
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Q1. Lot of questions on classification and regression models, medium python and pandas, no need of DSA
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Q2. Questions from assignment (first step will be to submit assignment given by them)
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