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.

Using a provided dataset, complete a full machine learning project. Tasks Choose an appropriate machine learning model for the dataset. Split data into training and testing sets. Train the model using Scikit-Learn. Evaluate performance using appropriate metrics. Present findings and recommendations in a written summary. Submission Jupyter Notebook PDF Report Model Evaluation Summary (accuracy, RMSE, or F1 score as appropriate)

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

You have been provided a CSV file containing student exam scores. Tasks Load the dataset using Pandas. Display the first 10 rows. Calculate average scores. Identify the highest-performing student. Write a short summary of your findings. Submission Python Notebook (.ipynb) PDF Report

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.