Airbnb Data Cleaning & Preprocessing Project 🧹📊

Welcome! This is my very first professional-grade project in the field of Data Analytics.

It represents my initial milestone in mastering the data science pipeline, where I focused intensely on the most critical phase: Data Cleaning and Preprocessing.

📌 Project Objective

In real-world analytics, the quality of insights depends entirely on the quality of the data. This project demonstrates a systematic approach to handling missing values, outliers, and complex string manipulations to prepare data for future predictive modeling.

🛠️ Tech Stack

• Language: Python
• Primary Library: Pandas (Data Manipulation)
• Supporting Libraries: NumPy, Seaborn, Matplotlib (Visualization of Data Quality)

🚀 Data Cleaning & Preprocessing Workflow

1. Initial Data Audit & Target Refinement

• Target Cleaning: Identified and removed records with null values in the price column to ensure dataset reliability.
• Handling Missing Values: - Performed a row-wise and column-wise null analysis.
  • Dropped columns with a high percentage of missing values (exceeding 50-60%) that couldn't provide meaningful signals.
  • Filtered out rows with critical missing information to reduce noise and improve data density.
• Redundancy Removal: Dropped irrelevant high-cardinality columns (URLs, IDs, scrape dates) to optimize memory usage.

2. Advanced Imputation Strategies

• Numerical Features: Applied median imputation to handle skewed distributions and outliers(e.g., host_listings_count).
• Categorical Features: Logically filled missing boolean indicators (e.g., host_is_superhost).
• Percentage Fields: Processed string-based percentages into numerical values and handled "Unknown" categories for missing response data.

3. Feature Engineering (Value Extraction)

• Text Feature Extraction: Created binary keyword indicators from listing descriptions (e.g., "spacious", "beach", "luxury").
• Temporal Features: Engineered host seniority (years active) and recency of reviews from date fields.
• Categorical Binning: Grouped continuous variables (like response rates) into discrete bins to simplify the feature space and improve model interpretability.

4. Data Distribution & Consistency

• Standardized boolean values 't'/'f' into binary numeric format.
• Used Histograms to verify that the cleaning process did not introduce biases in the review score distributions.

📊 Final Result

The output of this project is a Cleaned Dataset with:

• Zero missing values in critical feature columns.
• 10+ new engineered features extracted from raw text and dates.
• Optimized data types for efficient modeling and analysis. This cleaned dataset is ready for applications such as price prediction, demand forecasting, and host performance analysis.

🏗️ How to Use

1. Clone the repo:

   git clone [https://github.com/qtracie/airbnb-data.git](https://github.com/qtracie/airbnb-data.git)

2. Install dependencies:

   pip install -r requirements.txt

3. Execute the script:

   python notebooks/airbnb-analysis.py