Pet Adoption Dataset Explorer (Streamlit App)

This project is a Streamlit-based exploratory data analysis (EDA) application designed for educational purposes in Data Science and Machine Learning courses.

The app provides a complete walkthrough from raw data inspection to correlation analysis, standardization, and Principal Component Analysis (PCA).

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Features

The application includes the following steps:

1. Dataset Overview

   • Dataset shape (rows & columns)
   • Column names

2. Raw Data Preview

   • Interactive row selection
   • Tabular visualization

3. Descriptive Statistics

   • Full summary using
     df.describe(include="all")
   • Covers numeric and categorical attributes

4. Data Type Inspection

   • Column-wise data type listing

5. Missing Value Analysis

   • Missing value counts per column

6. Numeric Feature Detection

   • Automatic detection of numeric attributes

7. Correlation Analysis

   • Pearson correlation matrix
   • Heatmap visualization using Seaborn

8. Standardization

   • Z-score normalization applied to numeric features
   • Separate standardized dataset preview

9. Principal Component Analysis (PCA)

   • PCA applied on standardized numeric features
   • First two principal components (PC1 & PC2)
   • Explained variance ratio
   • 2D scatter plot visualization