Learn how to apply Principal Component Analysis (PCA) in machine learning for dimensionality reduction. In this step-by-step tutorial, we’ll cover the basics of PCA, its importance, and how to implement it using Python. Ideal for beginners and data enthusiasts looking to optimize their models and improve performance.
00:00 – Introduction Overview of PCA (Principal Component Analysis) and Machine Learning
02:00 – Data Preparation Importing Pandas and Loading the Dataset
06:00 – Feature Analysis Exploring Dataset Features
08:43 – Data Visualization Visualizing the Dataset in 1D and 2D
11:22 – Graphical Insights Plotting Graphs for Better Understanding
16:00 – Target Variables Understanding the Target Data
20:44 – Descriptive Analysis Describing the Dataset for Insights
24:01 – Data Scaling Scaling the Dataset for Machine Learning Models
32:13 – Train-Test Split Splitting Data: 80% Train, 20% Test
36:41 – Logistic Regression Applying Logistic Regression to the Digits Dataset
41:25 – Dimensionality Reduction Using PCA to Reduce Features
47:24 – Variance Explanation Understanding PCA Variance Ratio
54:08 – Model with PCA Features Applying Logistic Regression on Reduced Dimensions
56:15 – Evaluation Comparing Actual Data vs Predicted Values