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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, True/False, Yes/No, etc.)
It estimates the probability that a given input belongs to a particular category.
The model calculates the odds of the event happening.
It uses a logistic function to map the input values to ...
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
Random forest is a type of ensemble learning method.
It builds multiple decision trees during training.
Each tree is built using a subset of the training data and a random subset of features.
The final prediction is made by averaging the predictions of all the individual trees.
Random...
Decision trees are a popular machine learning algorithm used for classification and regression tasks.
Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.
They are easy to interpret and visualize, making them popular for exploratory data analysis.
Decision trees can handle both numerical ...
In the next 5 years, I see myself growing into a senior data scientist role, leading projects and mentoring junior team members.
Continuing to enhance my skills in data analysis, machine learning, and programming languages such as Python and R
Taking on more responsibilities in project management and client interactions
Working towards becoming a subject matter expert in a specific industry or domain
Mentoring and guiding ...
I was a student pursuing my undergraduate degree in Computer Science.
5 years back, I was studying Computer Science in college.
Now, I have completed my degree and gained experience in data science through internships and projects.
I have developed strong analytical and programming skills over the past 5 years.
I have also learned new technologies and tools in the field of data science.
I have a better understanding of real
I applied via Naukri.com and was interviewed in Jul 2017. There were 5 interview rounds.
I applied via Naukri.com and was interviewed in Jul 2017. There were 4 interview rounds.
I applied via Company Website and was interviewed in May 2021. There was 1 interview round.
I applied via Walk-in and was interviewed before Jul 2020. There were 3 interview rounds.
I applied via Naukri.com and was interviewed before Jul 2021. There were 3 interview rounds.
Convenience power, Patience
I applied via Campus Placement and was interviewed before Jun 2022. There were 6 interview rounds.
Python ML short project to categories individuals based on salary
Good and bad aspects of Data Science
Good: Data science helps in making informed decisions based on data-driven insights
Good: Data science can uncover valuable patterns and trends in large datasets
Bad: Data science can be time-consuming and resource-intensive
Bad: Data science may face challenges with data privacy and ethical considerations
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