Download the Titanic or Iris dataset from a public source. Clean the data (handle missing values and data types), then create 3 visualizations: a bar chart, a histogram, and a scatter plot. Write a short 200-word summary of your key findings. Submit your Jupyter Notebook (.ipynb) file.

You have been provided a CSV file containing student exam scores.

Tasks

1. Load the dataset using Pandas.
2. Display the first 10 rows.
3. Calculate average scores.
4. Identify the highest-performing student.
5. Write a short summary of your findings.

Submission

• Python Notebook (.ipynb)
• PDF Report

Analyze a retail sales dataset and create visualizations that communicate key business insights.

Tasks

• Create one bar chart showing category performance
• Create one line chart showing sales over time
• Create one histogram showing value distribution

Provide written insights explaining:

• Sales trends
• Top-performing categories
• Seasonal patterns

Submission

• Jupyter Notebook
• PDF Export
• Charts as PNG files

Using a provided dataset, complete a full machine learning project.

Tasks

1. Choose an appropriate machine learning model for the dataset.
2. Split data into training and testing sets.
3. Train the model using Scikit-Learn.
4. Evaluate performance using appropriate metrics.
5. Present findings and recommendations in a written summary.

Submission

• Jupyter Notebook
• PDF Report
• Model Evaluation Summary (accuracy, RMSE, or F1 score as appropriate)

Create a personal portfolio page using HTML & CSS. It must include at least 3 sections: About Me, Projects, and Contact. Use at least one Google Font, apply a color scheme using CSS variables, and make sure the layout uses Flexbox. Submit your HTML and CSS files as a ZIP.