Exploring Data Science with Python

What is the purpose of data cleaning in data science?

• To enhance data visualization techniques.

• To increase data storage capacity.

• The purpose of data cleaning in data science is to improve data quality for accurate analysis.

• To automate data collection processes.

Which library in Python is commonly used for data visualization?

• Pandas

• Matplotlib

• Scikit-learn

• NumPy

What function in Matplotlib is used to create a line plot?

• draw()

• plot()

• line()

• graph()

How can you handle missing values in a dataset?

• Use only complete cases

• Duplicate the dataset

• Ignore missing values

• Remove, fill, interpolate, predict, or flag missing values.

What is the difference between a bar chart and a histogram?

• A bar chart displays trends over time; a histogram shows categories.

• A bar chart is for categorical data; a histogram is for numerical data distribution.

• A bar chart is always vertical; a histogram is always horizontal.

• A bar chart uses continuous data; a histogram uses discrete data.

Which Pandas function is used to calculate the mean of a DataFrame column?

• mean()

• average()

• median()

• sum()

What is the purpose of the 'groupby' function in Pandas?

• To sort data in ascending order.

• To filter out specific rows from a DataFrame.

• To merge two DataFrames together.

• To group data for aggregation or transformation.

What is the significance of the train-test split in machine learning?

• The train-test split helps evaluate model performance and generalization.

• It helps in data preprocessing and cleaning.

• It is used to increase the size of the dataset.

• It determines the optimal number of features to use.

What is overfitting in the context of machine learning?

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

• Overfitting is when a model performs poorly on both training and unseen data due to lack of data.

• Overfitting refers to a model that generalizes well to new data but fails on training data.

Which algorithm is commonly used for classification tasks?

• Principal Component Analysis

• K-Means Clustering

• Logistic Regression

• Support Vector Machine