Synchrony Analytics Manager Interview Questions and Answers

I was asked Python, sql, coding questions

Case study on how would you identify the total number of footfall on a airport

Q1. Define your dataset and what difficulties you have faced while preparing your model ?
Ans. 
The dataset consists of customer purchase history and demographic information. Difficulties faced include data cleaning and missing values.
• Dataset includes customer ID, purchase amount, purchase date, age, gender, and location.

• Difficulties faced include handling missing values in the age and location columns.

• Data cleaning involved removing duplicates and outliers to ensure accurate analysis.

• Normalization and standardiz
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Q2. What is Normalization and standardization
Ans. 
Normalization and standardization are techniques used to rescale data to have a mean of 0 and a standard deviation of 1.
• Normalization is the process of rescaling the data to have values between 0 and 1.

• Standardization is the process of rescaling the data to have a mean of 0 and a standard deviation of 1.

• Normalization is useful when the features have different ranges.

• Standardization is useful when the features have diff...read more
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Q1. Work done in previous companty
Ans. 
Developed machine learning models to predict customer churn and optimize marketing campaigns.
• Built predictive models using Python and scikit-learn

• Utilized SQL to extract and manipulate data for analysis

• Collaborated with cross-functional teams to implement data-driven solutions
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Q1. How to check model performance? Over fit vs underfit?
Ans. 
Model performance can be checked using various metrics such as accuracy, precision, recall, F1 score, and confusion matrix.
• Split data into training and testing sets

• Train the model on the training set

• Evaluate the model on the testing set using metrics such as accuracy, precision, recall, F1 score, and confusion matrix

• If the model performs well on the testing set, it is not overfit or underfit

• If the model performs well o...read more
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Python code for 45 mins. Pandas , group by , filtering questions

Q1. Mostly behavioral questions
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