🏠 House Price Data Cleaning Script

Clean and preprocess the house price dataset from an Excel file, preparing it for further analysis or modeling by removing invalid, missing, or non-numeric data.

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📂 Dataset

• Input: HousePricePrediction.xlsx (Excel file, Sheet1)
• Output: Cleaned_HousePricePrediction.xlsx (cleaned and saved Excel file)

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🧹 Data Cleaning Steps

1. Initial Exploration Display dataset info and summary statistics.

2. Filter Invalid Records Remove rows with zero or negative values in critical columns:

   • LotArea
   • YearBuilt
   • YearRemodAdd
   • TotalBsmtSF
   • SalePrice

3. Handle Missing Data Drop rows containing any NaN values.

4. Keep Numeric Columns Only Remove all non-numeric columns to ensure clean data for modeling.

5. Save Cleaned Dataset Export the processed data to Cleaned_HousePricePrediction.xlsx.

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🚀 How to Use

1. Place your input Excel file at the correct path (file_path).

2. Install dependencies:

   pip install pandas openpyxl

3. Run the cleaning script:

   python house_price_cleaning.py

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📊 Output

• A clean, preprocessed Excel file: Cleaned_HousePricePrediction.xlsx.
• Console report showing the count of records remaining after cleaning.

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🎯 Why Use This Script?

• Automates tedious data cleaning steps.
• Ensures dataset integrity by removing invalid or missing data.
• Prepares data perfectly for machine learning models or further analysis.