Association rule mining allows you to uncover hidden relationships in traffic patterns that may not be immediately obvious. For example: Traffic Impact Factors: Determine how variables such as holidays, weather, and specific times of day affect traffic volume. Pattern Prediction: Identify situations where traffic is likely to be high or low based on historical data. Optimization: Enable better resource allocation, such as adjusting traffic signals, deploying road personnel, or planning infrastructure improvements.

Principal Component Analysis (PCA) is a powerful statistical technique used to extract meaningful patterns from high-dimensional or noisy data. In the context of noise removal and signal reconstruction, PCA helps isolate the true signal from the noise, making it easier to analyze and visualize the underlying patterns.

Clustering is a type of machine learning technique used to group similar data points or objects into clusters or groups, where data points in the same cluster are more similar to each other than to those in other clusters. The goal of clustering is to find inherent patterns or structures in data without any pre-labeled outcomes or target variables.Clustering as a technique can be used to analyze and compare the color distribution within two paintings (a real painting and a fake one). The idea is that real and fake paintings might have different color distributions due to differences in how the paintings were created, how colors were applied, or even how they have aged. These differences can be subtle but measurable using clustering techniques.

This document focuses on what dimension reduction is , its types and a comparative analysis of PCA and t-SNE (two dimension reduction techniques). The data used for the analysis is data from world bank's official site focusing on world development indicators.

This is my USL project for analysing data using different clustering techniques and make some meaningful conclusions