A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset.The dataset contains 32 features.
Which model will meet the business requirement?
Logistic regression
Linear regression
K-means
Principal component analysis (PCA)
Explanations:
Logistic regression is used for classification tasks, not for predicting continuous values like housing prices.
Linear regression is suitable for predicting continuous values, such as housing prices, based on the dataset features.
K-means is a clustering algorithm, which groups data into clusters, not suitable for predicting continuous values like housing prices.
PCA is a dimensionality reduction technique, not a predictive model. It helps reduce the number of features, but doesn’t directly predict prices.
I conclude that the answer is:
Linear regression
In my experience, the answer is:
Linear regression