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Handled missing variables by imputing with mean/median, using predictive models, or dropping rows/columns.
Impute missing values with mean or median for numerical variables
Use predictive models like random forest or KNN to impute missing values
Drop rows or columns with a high percentage of missing values
Understanding outliers, missing values, overfitting, and underfitting in data science.
Outliers are data points that significantly differ from other data points in a dataset.
Missing values are data points that are not present in a dataset.
Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.
Underfitting occurs when a model is too simple to capture the underlying pattern in...
I appeared for an interview in Feb 2025, where I was asked the following questions.
Advanced Excel functions enhance data analysis through powerful tools like VLOOKUP, INDEX, MATCH, and pivot tables.
VLOOKUP: Searches for a value in the first column of a range and returns a value in the same row from a specified column. Example: =VLOOKUP(A2, B2:D10, 3, FALSE)
INDEX & MATCH: A powerful combination for flexible lookups. Example: =INDEX(B2:B10, MATCH(A2, A2:A10, 0))
Pivot Tables: Summarize large dataset...
posted on 25 Oct 2024
posted on 15 Feb 2025
I appeared for an interview in Jan 2025.
Aptitude test aptitude test
posted on 20 Mar 2025
I appeared for an interview in Feb 2025, where I was asked the following questions.
posted on 14 Oct 2024
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
Supervised learning requires a target variable to predict, while unsupervised learning does not.
In supervised learning, the model learns from the labeled training data and makes predictions on new data. In unsupervised learning, the model finds patterns and relationships in the data without guidance.
Examples of sup...
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Aakash Educational Services
Frankfinn Institute of Air Hostess Training
NIIT
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