titanic-task1

🚢 Titanic Dataset - Data Cleaning & Preprocessing

This repository contains the code and outputs for Task 1 of the Elevate Labs AI & ML Internship. The task focuses on data cleaning and preprocessing using the famous Titanic dataset.

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📌 Task Objective

Clean and prepare raw Titanic data for Machine Learning models through data exploration, handling missing values, encoding categorical variables, and feature scaling.

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📂 Files in This Repo

File	Description
titanic.csv	Raw dataset (downloaded from Kaggle)
titanic_cleaned.csv	Cleaned version after preprocessing
titanic_preprocessing.ipynb	Jupyter Notebook with full code and outputs
README.md	Project overview and explanation

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🧪 Tools & Libraries Used

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn

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🔍 Step-by-Step Process

✅ 1. Data Exploration

• Displayed basic dataset information (.info(), .describe())
• Counted missing values

✅ 2. Handling Missing Values

• Filled Age using median
• Filled Embarked using mode
• Dropped Cabin due to too many nulls

✅ 3. Encoding Categorical Variables

• Label encoded Sex
• One-hot encoded Embarked

✅ 4. Feature Scaling

• Standardized Age and Fare using StandardScaler

✅ 5. Outlier Detection & Removal

• Visualized outliers with boxplots
• Removed outliers from Fare using the IQR method

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📊 Result

• Cleaned dataset with no missing values
• Encoded and scaled features
• Ready for model training

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📎 Dataset Source

• Titanic Dataset on Kaggle

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✅ Submission

Submitted GitHub repo as part of Task 1: Data Cleaning & Preprocessing for Elevate Labs Internship.