🧼 Titanic Data Cleaning Script

This project contains a Python script for preprocessing the original Titanic dataset (titanic_original.csv). The goal is to clean and prepare the data for further analysis or machine learning tasks.

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📄 Dataset

• Input file: titanic_original.csv
• Output file: titanic_cleaned.xlsx (cleaned version saved in Excel format)

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🧹 Cleaning Steps

1. Drop Unnecessary Columns The following columns were removed as they contain many missing values or are not relevant for modeling:

   • cabin
   • boat
   • body
   • home.dest

2. Handle Missing Values in Age

   • Missing values in the age column were filled using the mean age.
   • All age values were rounded to the nearest integer.

3. Fix Missing Embarked Values

   • Missing values in the embarked column were filled with 'S', the most frequent port of embarkation.

4. Correct Fare Values

   • Zero or negative fare values were replaced with the mean fare of positive fares only.
   • Negative fare values were clamped to 0.

5. Remove Duplicates

   • Duplicate records were identified and removed from the dataset.

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💾 Output

• Cleaned dataset saved as: titanic_cleaned.xlsx
• Format: Excel (uses openpyxl engine)

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▶️ How to Use

1. Install Dependencies

pip install pandas openpyxl

2. Run the Script

Ensure the titanic_original.csv file is in the same directory, then run:

python titanic_cleaning.py

After execution, titanic_cleaned.xlsx will be generated in the same directory.

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📌 Notes

• The script is designed to be a lightweight and simple preprocessor for Titanic data.
• It’s easily extensible for further cleaning or feature engineering.