feat: add lab2 programs on PCA

Signed-off-by: GitHub <noreply@github.com>

Lab2/README.md

@@ -0,0 +1,52 @@
+# Principal Component Analysis
+
+- Principal Component Analysis is a
+popular unsupervised learning technique
+for reducing the dimensionality of large
+data sets.
+- It increases interpretability yet,
+at the same time, it minimizes information
+loss. It helps to find the most significant
+features in a dataset and makes the data
+easy for plotting in 2D and 3D. PCA helps
+in finding a sequence of linear combinations
+of variables.
+
+## Terms
+
+- **Principal Component:** They are a straight line that captures most
+of the variance of the data. They have a direction and
+magnitude. Principal components are orthogonal projections
+(perpendicular) of data onto lower-dimensional space.
+
+- **Dimensionality:** Quantity of features or variables
+used in the research.
+
+## Steps
+
+1. **Standardize the data**: PCA requires standardized data, so the first step is to standardize the
+data to ensure that all variables have a mean of 0 and a standard deviation of 1.
+
+2. **Calculate the covariance matrix**: The next step is to calculate the covariance matrix of the
+standardized data. This matrix shows how each variable is related to every other variable in
+the dataset.
+
+3. **Calculate the eigenvectors and eigenvalues**: The eigenvectors and eigenvalues of the
+covariance matrix are then calculated. The eigenvectors represent the directions in which
+the data varies the most, while the eigenvalues represent the amount of variation along
+each eigenvector.
+
+4. **Choose the principal components**: The principal components are the eigenvectors with the
+highest eigenvalues. These components represent the directions in which the data varies
+the most and are used to transform the original data into a lower-dimensional space.
+
+5. **Transform the data**: The final step is to transform the original data into the
+lower-dimensional space defined by the principal components.
+
+## Applications
+
+- Used to visualize multidimensional data.
+- Used to reduce the number of dimensions in healthcare data.
+- Can help resize an image.
+- Used in finance to analyze stock data and forecast returns.
+- Helps to find patterns in the high-dimensional datasets.

README.md

@@ -13,6 +13,12 @@ This repository contains the programs that I worked out in Machine Learning Labo
   - [Introduction to Numpy and Pandas package](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/Numpy_&_Pandas.ipynb)
   - [Insurance Data Analysis](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/ML_Lab1_Insurance.ipynb)
   - [Iris Data Analysis](https://github.com/kbdharun/ML-Lab/blob/main/Lab1/ML_Lab1_Iris.ipynb)
+- Lab 2: Principal Component Analysis
+  - [About PCA](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/README.md)
+  - [PCA based dimensionality reduction on Wine dataset](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-DR-Wine.ipynb)
+  - [PCA using algorithm steps without `sklearn`](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-using-alg-without-sk.ipynb)
+  - [PCA using `sklearn` on Iris dataset](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-using-sklearn-Iris.ipynb)
+  - [PCA - Wine Quality Classification](https://github.com/kbdharun/ML-Lab/blob/main/Lab2/PCA-Wine-quality-classification.ipynb)
 
 ## Prerequisites
 

requirements.txt

@@ -1,6 +1,8 @@
-pandas
-numpy
 matplotlib
+notebook
+numpy
+pandas
+plotly
 seaborn
 scikit-learn
-notebook
+