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Tredence Data Scientist Interview Questions and Answers

Updated 6 Sep 2024

Q1. Transpose a matrix in python and machine learning questions

Ans.

To transpose a matrix in Python, use numpy.transpose() or the T attribute.

  • Use numpy.transpose() function to transpose a matrix.

  • Alternatively, use the T attribute of a numpy array.

  • Example: np.transpose(matrix) or matrix.T

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Q2. Evaluation metrics, AU ROC Curve, explain one use case

Ans.

AU ROC Curve is used to evaluate the performance of classification models by measuring the trade-off between true positive rate and false positive rate.

  • AU ROC Curve is commonly used in binary classification problems.

  • It helps in comparing different models based on their ability to distinguish between classes.

  • The area under the ROC curve (AUROC) value closer to 1 indicates a better model performance.

  • For example, in healthcare, AU ROC Curve can be used to evaluate the performanc...read more

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Q3. How to handle missing values

Ans.

Handle missing values by imputation, deletion, or using algorithms that can handle missing data.

  • Impute missing values using mean, median, mode, or predictive modeling

  • Delete rows or columns with missing values if they are insignificant

  • Use algorithms like XGBoost, Random Forest, or LightGBM that can handle missing data

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Q4. What are numpy and pandas

Ans.

NumPy is a library for numerical computing in Python, while Pandas is a data manipulation and analysis tool.

  • NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

  • Pandas offers data structures like DataFrame for easy data manipulation and analysis, with tools for reading and writing data from various file formats.

  • Both libraries are commonly used in data science and machine learning ...read more

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Q5. Interpretation of logistic regression

Ans.

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

  • Logistic regression is used when the dependent variable is binary (0/1, yes/no, true/false, etc.)

  • It estimates the probability that a given observation belongs to a particular category.

  • The output of logistic regression is a probability score between 0 and 1.

  • It uses the logistic function (sigmoid function) to model the relationship between the ...read more

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Q6. Visualization libraries in python

Ans.

Python has various visualization libraries like Matplotlib, Seaborn, Plotly, and Bokeh.

  • Matplotlib is a widely used library for creating static, interactive, and animated plots.

  • Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics.

  • Plotly is great for creating interactive plots and dashboards.

  • Bokeh is another interactive visualization library that targets modern web browsers for presentation.

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Q7. Precession vs recall?

Ans.

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.

  • Precision is the ratio of correctly predicted positive observations to the total predicted positives.

  • Recall is the ratio of correctly predicted positive observations to all actual positives.

  • Precision is important when the cost of false positives is high, while recall is important when the cost of false negatives is high.

  • F1 score combines precision and reca...read more

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Interview Process at Tredence Data Scientist

based on 10 interviews
2 Interview rounds
Coding Test Round
Technical Round
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