Develop a predictive model for a given dataset. Aim to optimize the model's accuracy and evaluate its performance using appropriate metrics.

The Predictive Modeling Challenge

Take a dirty or messy dataset and clean it up. This could include handling missing data, dealing with outliers, or normalizing fields.

The Data Cleaning Challenge

Create new features from an existing dataset to improve a model's performance. This could involve feature extraction or feature transformation.

Visualize a complex dataset in a clear and intuitive way. This could involve using tools like Matplotlib, Seaborn, or Tableau.

Apply unsupervised learning techniques to a dataset. This could involve clustering, dimensionality reduction, or anomaly detection.

Perform a natural language processing task on a text dataset. This could involve sentiment analysis, topic modeling, or named entity recognition.

Analyze a time-series dataset. This could involve forecasting, anomaly detection, or trend analysis.

Design and analyze an A/B test. Consider the test's statistical power, the effect size, and how you would handle multiple comparisons.

Process and analyze a large dataset. This could involve using tools like Hadoop, Spark, or distributed databases.

Implement a deep learning model for a complex task. This could involve image classification, text generation, or reinforcement learning.

Use historical data to predict future trends.

Segment a company's customers based on their behavior.

Analyze social media data to gauge public opinion on a topic.

Create a system that recommends products to customers.

Detect fraudulent activities using machine learning.