• СреднийУровень мастерства
• 4.2+
• 341

Use logistic regression to predict whether people will make a claim on their car insurance!

• СреднийУровень мастерства
• 4.0+
• 322

Perform a hypothesis test to determine if more goals are scored in womens soccer matches than mens!

• ПередовойУровень мастерства
• 4.4+
• 319

Help the bank monitoring their fraud detection model and figuring out why its not performing as expected.

• СреднийУровень мастерства
• 4.4+
• 314

Ready to analyze and visualize financial ratios? In this project, you will take on real-world challenges like evaluating the profitability and leverage of companies across industries.

• СреднийУровень мастерства
• 3.9+
• 308

Use web scraping and NLP to find the most frequent words in classic literature: Herman Melvilles novel, Moby Dick.

• ПередовойУровень мастерства
• 4.5+
• 306

Perform a machine learning experiment to find the best model that predicts the temperature in London!

• ПередовойУровень мастерства
• 3.8+
• 305

Develop an inventory management system for a retail business using OOP in Python.

• СреднийУровень мастерства
• 3.0+
• 300

Examine flight delays & cancellations to uncover insights.

• БазовыйУровень мастерства
• 4.1+
• 288

Use OpenAIs powerful API to enrich and summarize stock market data.

• СреднийУровень мастерства
• 3.9+
• 287

Use MLBs Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.

• БазовыйУровень мастерства
• 4.3+
• 282

Build interactive stock charts with Plotly and uncover financial trends in the fast food industry.

• БазовыйУровень мастерства
• 4.0+
• 274

Create custom Python functions to validate user input!

• СреднийУровень мастерства
• 4.0+
• 266

Build an AI-powered inbox assistant to classify your emails using Llama.

• БазовыйУровень мастерства
• 3.9+
• 263

Exploring flight data from NYC using advanced data joining techniques in R.

• ПередовойУровень мастерства
• 3.9+
• 259

Use deep learning to automate the classification of service desk tickets.

• СреднийУровень мастерства
• 4.0+
• 256

Chart electric vehicle charging trends to inform strategic planning.

• СреднийУровень мастерства
• 3.9+
• 256

Build models predicting customer churn for Indian telecom customers.

• БазовыйУровень мастерства
• 4.2+
• 248

Use SQL to analyze a database containing information about Transport for London journeys over 12 years!

• СреднийУровень мастерства
• 4.4+
• 247

Powering Data for the Department of Energy - Building an ETL Pipeline

• ПередовойУровень мастерства
• 4.3+
• 245

Transcribe customer support audio calls, evaluate sentiment, search in text and identify common entities to improve customer service!

• БазовыйУровень мастерства
• 4.1+
• 244

Use a variety of data manipulation techniques to explore different aspects of Legos history.

• СреднийУровень мастерства
• 3.9+
• 237

Query the VatComply API to gather exchange rate information and incorporate into a sales orders dataset!

• СреднийУровень мастерства
• 4.0+
• 235

Use mean-variance optimization to find optimal portfolio weights and then check how well they would have performed.

• СреднийУровень мастерства
• 4.5+
• 231

Explore facial recognition with ML by distinguishing Arnold Schwarzenegger from others using Python and scikit-learn.

• СреднийУровень мастерства
• 3.3+
• 229

Use joining techniques to discover the oldest businesses in the world.

• ПередовойУровень мастерства
• 4.5+
• 224

Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.

• СреднийУровень мастерства
• 4.1+
• 208

Analyze online shopping habits: returning vs. new customers using statistics & probability techniques!

• БазовыйУровень мастерства
• 4.2+
• 207

Automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.

• ПередовойУровень мастерства
• 4.1+
• 198

Explore transportation time series data analysis to uncover patterns and forecast travel costs.

• СреднийУровень мастерства
• 4.5+
• 181

Generate features from text and numeric variables to predict the popularity of books.