Impact Analytics Interview Questions, Process, and Tips

Q1. 2 Technical rounds were great and the panel was very welcoming.
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Q2. Modelling and Python sql
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Q1. Python and resume
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Q1. Bakwas round. they took a month to schedule. didn't attend at their mentioned time 4 times. HR are worst including the Manager who interviewed. No respect for the candidates in hr round. They'll call to o...read more
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Q2. Resume. and you can't ask challenging questions at the end. Cuz they'll disqualify you
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React and Java Script concepts

Q1. Questions from react advanced concepts
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Q1. HR related questions
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Q1. What are different types of regression analysis ?
Ans. 
Different types of regression analysis include linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression.
• Linear regression: Predicts a continuous outcome based on one or more input features.

• Logistic regression: Predicts the probability of a binary outcome.

• Polynomial regression: Fits a curve to the data by including polynomial terms.

• Ridge regression: Adds a penalty term to the...read more
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Q2. What is confusion matrix?
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Confusion matrix is a table used to evaluate the performance of a classification model.
• It is a matrix with rows representing the actual class and columns representing the predicted class.

• It helps in visualizing the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions.

• It is commonly used in machine learning to assess the quality of th...read more
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Q3. Questions around python programming
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Q1. Explain the architecture of Transformer based models.
Ans. 
Transformer based models use self-attention mechanism to capture long-range dependencies in data.
• Transformer models consist of encoder and decoder layers.

• Self-attention mechanism allows each word to attend to all other words in the input sequence.

• Positional encoding is added to input embeddings to provide information about the position of words.

• Transformer models have achieved state-of-the-art results in various NLP ta...read more
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Q2. Different NLP techniques around extraction of text.
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Various NLP techniques for text extraction include Named Entity Recognition, Part-of-Speech tagging, and Dependency Parsing.
• Named Entity Recognition (NER) identifies entities such as names, dates, and locations in text.

• Part-of-Speech tagging assigns grammatical categories to words in a sentence.

• Dependency Parsing analyzes the grammatical structure of a sentence to identify relationships between words.
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Q1. Medium level js and react questions
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