Arcadia Consulting Services Data Scientist Interview Questions and Answers

Q1. ML, DL, Data Science, Python
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Q1. AI,ML, Data Science, Use cases
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Q1. What is Linear regression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What li list and tuple
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List and tuple are both data structures in Python, but list is mutable while tuple is immutable.
• List is denoted by square brackets [] while tuple is denoted by parentheses ().

• Elements in a list can be changed, added, or removed, while elements in a tuple cannot be changed once defined.

• Lists are used when you need a collection of items that may change, while tuples are used for fixed collections of items.

• Example: list =
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Q3. Previous company project experience
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Developed a predictive analytics model to forecast customer churn for a telecom company.
• Utilized machine learning algorithms such as logistic regression and random forests

• Cleaned and preprocessed large datasets to improve model accuracy

• Collaborated with cross-functional teams to gather domain knowledge and insights
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Combination logic on python

Q1. Explain classification
Ans. 
Classification is a machine learning technique used to categorize data into different classes or categories based on past observations.
• Classification involves training a model on labeled data to predict the class of new, unseen data points.

• Common algorithms for classification include logistic regression, decision trees, support vector machines, and k-nearest neighbors.

• Examples of classification tasks include spam email...read more
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Q1. What is stemming and lematization
Ans. 
Stemming and lemmatization are techniques used in natural language processing to reduce words to their base or root form.
• Stemming is a process of reducing words to their base form by removing suffixes.

• Lemmatization is a process of reducing words to their base form by considering the context and part of speech.

• Stemming is faster but may not always produce a valid word, while lemmatization is slower but produces valid wo...read more
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Q2. Python coding about default values in class and funtions
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Q3. Kmeans clustering evaluation
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Q4. How to measure multicollinearity
Ans. 
Multicollinearity can be measured using correlation matrix, variance inflation factor (VIF), or eigenvalues.
• Calculate the correlation matrix to identify highly correlated variables.

• Use the variance inflation factor (VIF) to quantify the extent of multicollinearity.

• Check for high eigenvalues in the correlation matrix, indicating multicollinearity.

• Consider using dimensionality reduction techniques like principal componen
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1 hour test with 3 python programming questions.

Q1. Are all the decision trees same in a random forest ?
Ans. 
No, decision trees in a random forest are different due to the use of bootstrapping and feature randomization.
• Decision trees in a random forest are trained on different subsets of the data through bootstrapping.

• Each decision tree in a random forest also considers only a random subset of features at each split.

• The final prediction in a random forest is made by aggregating the predictions of all individual decision trees
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Q2. How will you handle class imbalanced dataset to increase the f1 score ?
Ans. 
Handling class imbalanced dataset involves techniques like resampling, using different algorithms, adjusting class weights, and using ensemble methods.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the...read more
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