This document discusses dimension reduction in machine learning. It introduces the team members working on the project and provides an overview of dimension reduction, including its importance in obtaining better features for classification or regression tasks. Several common methods for performing dimension reduction are described, such as missing values, low variance, decision trees, and random forest. The benefits of dimension reduction include faster computation, data compression, improved model performance, better data visualization, and noise removal.

2. Team Members
» Sabbir Ahmed Saikat
ID-13142103001
» Ayatul Ahammed
ID-13142103044

3. Index
Introduction
Importance
Types of Dimensions
Methods to Perform Reduction
Benefits

4. What is Dimension Reduction??
Dimension Reduction refers to the process of
converting a set of data having vast dimensions into
data with lesser dimensions ensuring that
it conveys similar information concisely.

5. Why Dimension Reduction is Important??
These techniques are typically used while
solving machine learning problems to obtain better
features for a classification or regression task.

6. 2D vs 1D

7. Some Methods to Perform Dimension Reduction
»Missing Values
»Low Variance
»Decision Trees
»Random Forest
»High Correlation

8. Benefits of Dimension Reduction
»It fastens the time required for performing same
computations. Less dimensions leads to less computing.
»It helps in data compressing and reducing the storage space
required.
»It takes care of multi-collinearity that improves the model
performance.
»Reducing the dimensions of data to 2D or 3D may allow us
to plot and visualize it precisely.
»It is helpful in noise removal also and as result of that we
can improve the performance of models.