PCA-Hierarchical-Clustering-Analysis

Questionnaire Analysis: PCA and Hierarchical Clustering

This project focuses on the analysis of questionnaire data using statistical techniques, including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA). The aim is to gain insights, identify patterns, and perform clustering based on the collected questionnaire responses.

Features

Data preprocessing:

Cleaning, transforming, and preparing the questionnaire data for analysis.

Descriptive statistical analysis:

Calculating summary statistics and exploring the distribution of variables.

Exploratory data analysis (EDA):

Visualizing the questionnaire data to understand the relationships and patterns.

Principal Component Analysis (PCA):

Performing PCA to reduce the dimensionality of the data and identify important factors.

Hierarchical Clustering Analysis (HCA):

Applying HCA to cluster similar responses based on their similarities or dissimilarities.

Cluster interpretation:

Interpreting and analyzing the clusters obtained from HCA to gain insights into different respondent groups.