Urban Sustainability Dataset – Data Cleaning, Exploratory Python Analysis Report
1. Data Cleaning & Preparation
Loaded the dataset using KaggleHub.
Inspected structure, verified datatypes, and performed initial quality checks.
Handled missing values, duplicates, and inconsistent entries.
Prepared the dataset for analysis through transformations and formatting.
2. Exploratory Data Analysis (EDA)
Displayed the first few records and summary statistics.
Explored key variables and trends using descriptive statistics.
Identified important relationships, variations, and patterns within the data.
3. Storytelling & Interpretation
Used clear Markdown-style explanations to walk through findings.
Ensured a smooth narrative flow to help readers understand insights.
Highlighted implications for sustainability and urban planning.
4. Creativity & Impact
Presented the dataset effectively for future modeling or visualization.
Structured the notebook so that additional analysis like clustering, correlation heatmaps, or predictive models can be added easily.