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I applied via Naukri.com and was interviewed in Sep 2021. There were 2 interview rounds.
Regression predicts continuous values while classification predicts categorical values.
Regression is used to predict a continuous output variable based on one or more input variables.
Classification is used to predict a categorical output variable based on one or more input variables.
Regression algorithms include linear regression, polynomial regression, and logistic regression.
Classification algorithms include decision...
Predictive modelling is the process of using statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
Predictive modelling involves analyzing historical data to identify patterns and relationships
These patterns are then used to create a model that can be used to predict future outcomes
The model is tested and refined using additional data to improve its accuracy
Predictiv...
NLP stands for Natural Language Processing and GDF Function is a tool used for data analysis.
NLP is a branch of AI that deals with the interaction between computers and humans using natural language.
It involves tasks such as sentiment analysis, language translation, and speech recognition.
GDF Function is a tool used in data analysis to create graphs and charts to visualize data.
It stands for Graph Definition File and i...
Top trending discussions
A pivot table in Excel is a data summarization tool that allows you to reorganize and summarize selected columns and rows of data.
Allows users to summarize and analyze large datasets
Can easily reorganize data by dragging and dropping fields
Provides options to calculate sums, averages, counts, etc. for data
Helps in creating interactive reports and charts
Useful for identifying trends and patterns in data
ETL stands for Extract, Transform, Load. It is a process used in data warehousing to extract data from various sources, transform it into a consistent format, and load it into a target database.
ETL stands for Extract, Transform, Load
Extract: Involves extracting data from various sources such as databases, applications, and files
Transform: Involves cleaning, filtering, and transforming the extracted data into a consiste...
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
A pivot table in Excel is a data summarization tool that allows you to reorganize and summarize selected columns and rows of data.
Allows users to summarize and analyze large datasets
Can easily reorganize data by dragging and dropping fields
Provides options to calculate sums, averages, counts, etc. for data
Helps in creating interactive reports and charts
Useful for identifying trends and patterns in data
Forecasting problem - Predict daily sku level sales
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Easy and moderate level question
Given a relevant topic and discussion on it
Reasoning and aptitude questions were asked
posted on 29 Feb 2024
I applied via Approached by Company and was interviewed before Mar 2023. There were 3 interview rounds.
based on 2 reviews
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