README.md

Titanic Data Cleaning and Preprocessing using Python Project Overview

This project demonstrates the complete data cleaning and preprocessing workflow using the Titanic dataset. The goal is to transform raw data into a clean, structured, and analysis-ready format by addressing common data quality issues such as missing values, duplicate records, inconsistent formats, and categorical variables.

The cleaned dataset can be used for exploratory data analysis (EDA) or as a foundation for machine learning models.

Objectives Load and inspect the dataset Handle missing values Remove duplicate records Standardize column names Clean text data Encode categorical variables Detect and analyze outliers Validate data quality Save the cleaned dataset for future analysis 📂 Dataset Information

The dataset contains passenger information from the Titanic disaster, including:

Passenger ID Survival Status Passenger Class Name Gender Age Number of Siblings/Spouses Number of Parents/Children Ticket Number Fare Cabin Port of Embarkation 🛠️ Data Cleaning & Preprocessing Steps

✔ Loaded and explored the dataset

✔ Inspected data types and descriptive statistics

✔ Checked for missing values

✔ Filled missing values in the Age column using the median

✔ Filled missing values in the Embarked column using the mode

✔ Removed the Cabin column due to a high percentage of missing values

✔ Checked for duplicate records

✔ Standardized column names

✔ Trimmed and standardized text values

✔ Encoded the Sex column into a binary feature (is_female)

✔ Applied one-hot encoding to the Embarked column

✔ Detected outliers using box plots and the IQR method

✔ Performed data validation checks

✔ Saved the cleaned dataset

📊 Exploratory Data Analysis

The notebook also includes:

Survival Distribution Passenger Class Distribution Gender Distribution Age Distribution Fare Distribution Correlation Heatmap 💻 Technologies Used Python Pandas NumPy Matplotlib Seaborn Jupyter Notebook