AI & ML Internship - Task 1

Data Cleaning & Preprocessing using Titanic Dataset

Objective

To clean and preprocess the Titanic dataset for Machine Learning applications.

Dataset

Titanic Dataset

Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-Learn

Steps Performed

1. Data Exploration

• Loaded the dataset using Pandas.
• Checked dataset shape, columns, and data types.
• Identified missing values.

2. Handling Missing Values

• Filled missing Age values using median.
• Filled missing Embarked values using mode.
• Replaced missing Cabin values with "Unknown".

3. Encoding Categorical Variables

• Converted Sex and Embarked columns into numerical values using Label Encoding.

4. Outlier Detection and Removal

• Visualized outliers using boxplots.
• Removed outliers using the IQR method.

5. Feature Scaling

• Standardized Age and Fare columns using StandardScaler.

6. Data Visualization

• Created boxplots for outlier detection.
• Generated a correlation heatmap.

7. Final Output

• Saved the cleaned dataset as cleaned_titanic.csv.

Files Included

• Titanic-Dataset.csv
• cleaned_titanic.csv
• task1.py
• README.md

Outcome

Successfully cleaned and preprocessed the dataset, making it suitable for Machine Learning models.