# How to extract features using PCA in Python

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of a dataset. It does this by finding the directions in which the data varies the most, and representing the data in terms of these directions. By representing the data in this way, it can be easier to visualize, understand, and analyze.

In Python, PCA can be performed using the scikit-learn library. Here are the steps to extract features using PCA in Python:

- Import the necessary libraries. You will need to have scikit-learn and numpy installed.

```
import numpy as np
from sklearn.decomposition import PCA
```

- Load your dataset. You can do this using any method that you prefer, such as pandas.

```
import pandas as pd
data = pd.read_csv("your_data.csv")
```

- Create a PCA object. You can specify the number of components you want to keep, or let the algorithm determine the number for you.

`pca = PCA(n_components=0.95)`

- Fit the PCA model to your data. This will find the principal components of the data.

`pca.fit(data)`

- Transform your data to the new principal component space.

`data_pca = pca.transform(data)`

- The new dataset is now reduced to the number of principal components that you specified or if you have used a ratio it will be reduced to the number of features that are needed to account for 95% of variance.

That’s it! You’ve successfully used PCA to extract features from your dataset in Python. Now you can use this new, lower-dimensional representation of your data for further analysis.

In this Learn through Codes example, you will learn: How to extract features using PCA in Python.

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