Ames Housing Data Analysis

Cleaned, structured, and validated a comprehensive real estate dataset comprising 2,930 property records across 82 distinct physical, structural, and financial attributes using Python in Google Colab.Environment Setup: Configured Google Colab with Google Drive integration for reproducible workflow execution.

Data Ingestion & Inspection: Loaded multi-column real estate Excel data using pandas, inspecting structural schemas, data types, and high-dimensional features (e.g., Lot Area, Land Contour, SalePrice).

Missing Value Analysis: Systematically computed null counts across all 82 variables using df.isnull().sum(), isolating feature-level gaps (such as 490 missing entries in Lot Frontage) to determine imputation strategies.

Data Readiness: Processed raw property features into a sanitized dataset ready for Exploratory Data Analysis (EDA) and valuation modeling.