def perform_pca(self, n_components): pca = PCA(n_components=n_components) self.pca_data = pca.fit_transform(self.scaled_data) def run_pipeline(self, n_components): self.load_data() self.scale_data() self.perform
How to Perform PCA (Principal Component Analysis) In practice, PCA is usually solved using Eigenvalue Decomposition [3] as this is computationally efficient. While many Python packages include built-in functions to perform PCA, let’s take what we’ve just learned in order to implement PCA: #...
Hands-on Time Series Anomaly Detection using Autoencoders, with Python Data Science Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read 3 AI Use Cases (That Are Not a Chatbot) ...
This reflects the real-world challenge where fraud is rare but critical to detect. Features: Time: The seconds elapsed between a transaction and the first transaction in the dataset. Amount: The monetary value of the transaction. V1 to V28: Principal components resulting from PCA (Principal ...
In this tutorial, you discovered how to develop and evaluate Lasso Regression models in Python. Specifically, you learned: Lasso Regression is an extension of linear regression that adds a regularization penalty to the loss function during training. How to evaluate a Lasso Regression model and use...
If you're short on time and want to know how to learn AI from scratch, check out our quick summary. Remember, learning AI takes time, but with the right plan, you can progress efficiently: Months 1-3: Build foundational skills in Python, math (linear algebra, probability, and statistics...
Following are the techniques to perform the dimensionality reduction: Feature Selection Feature Extraction If you are not sure about the PCA (principal component analysis )and the need for dimensionality reduction, don't worry. You are in the right place. In this article, we are going to cover...
We can then train a projection method on the vectors, such as those methods offered in scikit-learn, then use matplotlib to plot the projection as a scatter plot. Let’s look at an example with Principal Component Analysis or PCA. Plot Word Vectors Using PCA We can create a 2-dimensional...
because even if a variable has a high VIF, it could still be very important for predicting the outcome. So if you remove it without checking its importance score, your model might perform worse. The key is to carefully evaluate whether the variable is essential before deciding to remove it....
Customer churn prediction model: I want you to act as a data scientist and perform feature engineering for a customer churn prediction model. The dataset contains customer [metrics]. Please write Python code to generate new features that could improve the model’s performance. ...