AB InBev India Data Scientist Interview Questions, Process, and Tips

Q1. What was your projrct about ?
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Q2. What approach did you use and why ?
Ans. 
I used a combination of supervised and unsupervised learning approaches to analyze the data.
• I used supervised learning to train models for classification and regression tasks.

• I used unsupervised learning to identify patterns and relationships in the data.

• I also used feature engineering to extract relevant features from the data.

• I chose this approach because it allowed me to gain insights from the data and make predicti
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Q3. Why was this model/ approach used instead of others ?
Ans. 
The model/approach was chosen based on its accuracy, interpretability, and scalability.
• The chosen model/approach had the highest accuracy compared to others.

• The chosen model/approach was more interpretable and easier to explain to stakeholders.

• The chosen model/approach was more scalable and could handle larger datasets.

• Other models/approaches were considered but did not meet the requirements or had limitations.

• The chos...read more
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Q4. How did you prevent your model from overfitting ? What did you do when it was underfit ?
Ans. 
To prevent overfitting, I used techniques like regularization, cross-validation, and early stopping. For underfitting, I tried increasing model complexity and adding more features.
• Used regularization techniques like L1 and L2 regularization to penalize large weights

• Used cross-validation to evaluate model performance on different subsets of data

• Used early stopping to prevent the model from continuing to train when perfo...read more
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If you were to design a tool that splits the budget across brands and vehicles, how would you go about this.
Apart from this, i was asked about why i was joining this company.

Q1. What are your career aspirations ?
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Q2. Couple of more case studies
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Q1. Explain chi square distribution. What are the assumptions involved?
Ans. 
Chi square distribution is a probability distribution used in statistical tests to determine the significance of relationships between categorical variables.
• Chi square distribution is a continuous probability distribution that is used in statistical tests such as the chi square test.

• It is skewed to the right and its shape is determined by the degrees of freedom.

• Assumptions involved in chi square distribution include: r...read more
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Q2. Explain different clustering algorithms
Ans. 
Clustering algorithms group similar data points together based on certain criteria.
• K-means: partitions data into K clusters based on centroids

• Hierarchical clustering: creates a tree of clusters

• DBSCAN: density-based clustering algorithm

• Mean Shift: shifts centroids to maximize data points within a certain radius

• Gaussian Mixture Models: assumes data points are generated from a mixture of Gaussian distributions
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Q1. Deep dive into clustering and bagging boosting algorithms
Ans. 
Clustering and bagging boosting algorithms are popular techniques in machine learning for grouping data points and improving model accuracy.
• Clustering algorithms like K-means, DBSCAN, and hierarchical clustering are used to group similar data points together based on certain criteria.

• Bagging algorithms like Random Forest create multiple subsets of the training data and train individual models on each subset, then combi...read more
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Q1. Different situation based questions
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Included aptitude, coding, english grammar, technical questions

Easy sql and python questions

Q1. Librarires Used
Ans. 
I have experience using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib for data analysis and visualization.
• Pandas for data manipulation

• NumPy for numerical operations

• Scikit-learn for machine learning algorithms

• Matplotlib for data visualization
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Q2. Previous work experiemce
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