Supervised learning is a method for clustering data points.

Unsupervised learning involves training models without labeled data.

Supervised learning is only applicable to image recognition tasks.

Supervised learning is a machine learning approach that uses labeled data to train models for making predictions.

A method that requires labeled data for training.

A process that predicts future outcomes based on past data.

A technique that only works with structured data.

Unsupervised learning is a machine learning approach that finds patterns in data without labeled outputs.

Train-test split is used to Test the given dataset.

Train-test split is a method to divide a dataset into training and testing subsets for model evaluation.

Train-test split is a technique for Model Training

Train-test split is a method to combine all data for training.

Linear regression is used for classifying categorical data.

Linear regression is used for predicting the value of a dependent variable based on the values of independent variables.

Linear regression is used for clustering similar data points.

Linear regression is used for analyzing time series data.

Simple linear regression analyzes categorical data; multivariate regression analyzes numerical data.

Simple linear regression is used for classification; multivariate regression is used for regression.

Simple linear regression uses one predictor; multivariate regression uses multiple predictors.

Simple linear regression uses multiple predictors; multivariate regression uses one predictor.

The purpose of a training dataset is to train a machine learning model.

To generate test data for evaluation.

To validate a machine learning model's performance.

To store the final model parameters.

A test dataset is for evaluation, while a training dataset is for learning.

A test dataset is used to create the model, while a training dataset is for testing the model.

A test dataset is for Finding MSE and RMSE

A test dataset is used for finding MSE and model training.

Y = β0X + ε

Y = α + βX

Y = β0 + β1X

Y = β1 + β0 + X

Overfitting is when a model learns the training data too well, leading to poor performance on new data.

Overfitting is when a model performs well on new data but poorly on training data.

Overfitting is the process of reducing the size of a model to improve its performance.

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

Features are the independent variables used to predict the dependent variable in a regression model.

Features are the dependent variables in a regression model.

Features are the constants that do not change in the model.

Features are used to measure the accuracy of the model.

Use only visual inspection of the data.

Evaluate based on the number of features used.

Check the training time of the model.

Use metrics like MAE, MSE, RMSE, and R-squared to evaluate performance.

Handle missing data

Rename columns

Convert data type

Improper encoding

Converting categorical variables into numerical form

Scaling numerical data to have a mean of 0 and a standard deviation of 1

Removing missing values from a dataset

Combining two datasets

Convert all values into a standard format

Remove all inconsistent data

Keep the data as it is

Replace with NaN

check_na()

isnull()

missing_values()

dropna()

To delete all missing values

To improve data quality and ensure accuracy

To increase dataset size

To convert categorical variables

Missing data, inconsistent data formats, duplicate data, outliers, and handling errors or inconsistencies in the data.

Data cleaning is not necessary, data cleaning is time-consuming, data cleaning is expensive

Lack of data quality standards, data duplication, data inconsistency, data normalization issues

Irrelevant data, incomplete data, incorrect data formats, handling missing values

To convert categorical variables into a binary format where each category is represented by a unique binary vector.

To reduce the dimensionality of numerical data.

To handle missing values in a dataset.

To normalize the data for better model performance.

Label encoding is a technique that converts categorical variables into numerical values by assigning each category a unique integer.

Label encoding is a way to visualize categorical data.

Label encoding is used to standardize numerical data.

Label encoding is a method to fill in missing values in a dataset.