Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns patterns from real data and creates high-quality synthetic data for safe sharing, testing, and model training. In this tutorial, we’ll use SDV to generate synthetic data step by step. pip install sdv We will first install the sdv library: from sdv.io.local import CSVHandler connector = CSVHandler() FOLDER_NAME = '.' # If the data is in the same directory data = connector.read(folder_name=FOLDER_NAME) salesDf = data['data'] Next, we import the necessary module and connect to our local folder containing the dataset files. This reads the CSV files from the specified folder and stores them as pandas DataFrames. In this case, we access the main dataset using data[‘data’]. from sdv.metadata import Metadata metadata = Metadata.load_from_json('metadata.json') We now import the metadata for our dataset. This metadata is stored in a JSON file and tells SDV how to interpret your data. It includes: The table name The primary key The data type of each column (e.g., categorical, numerical, datetime, etc.) Optional column formats like datetime patterns or ID patterns Table relationships (for multi-table setups) Here is a sample metadata.json format: { "METADATA_SPEC_VERSION": "V1", "tables": { "your_table_name": { "primary_key": "your_primary_key_column", "columns": { "your_primary_key_column": { "sdtype": "id", "regex_format": "T[0-9]{6}" }, "date_column": { "sdtype": "datetime", "datetime_format": "%d-%m-%Y" }, "category_column": { "sdtype": "categorical" }, "numeric_column": { "sdtype": "numerical" } }, "column_relationships": [] } } } from sdv.metadata import Metadata metadata = Metadata.detect_from_dataframes(data) Alternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies. from sdv.single_table import GaussianCopulaSynthesizer synthesizer = GaussianCopulaSynthesizer(metadata) synthesizer.fit(data=salesDf) synthetic_data = synthesizer.sample(num_rows=10000) With the metadata and original dataset ready, we can now use SDV to train a model and generate synthetic data. The model learns the structure and patterns in your real dataset and uses that knowledge to create synthetic records. You can control how many rows to generate using the num_rows argument. from sdv.evaluation.single_table import evaluate_quality quality_report = evaluate_quality( salesDf, synthetic_data, metadata) The SDV library also provides tools to evaluate the quality of your synthetic data by comparing it to the original dataset. A great place to start is by generating a quality report You can also visualize how the synthetic data compares to the real data using SDV’s built-in plotting tools. For example, import get_column_plot from sdv.evaluation.single_table to create comparison plots for specific columns: from sdv.evaluation.single_table import get_column_plot fig = get_column_plot( real_data=salesDf, synthetic_data=synthetic_data, column_name='Sales', metadata=metadata ) fig.show() We can observe that the distribution of the ‘Sales’ column in the real and synthetic data is very similar. To explore further, we can use matplotlib to create more detailed comparisons—such as visualizing the average monthly sales trends across both datasets. import pandas as pd import matplotlib.pyplot as plt # Ensure 'Date' columns are datetime salesDf['Date'] = pd.to_datetime(salesDf['Date'], format='%d-%m-%Y') synthetic_data['Date'] = pd.to_datetime(synthetic_data['Date'], format='%d-%m-%Y') # Extract 'Month' as year-month string salesDf['Month'] = salesDf['Date'].dt.to_period('M').astype(str) synthetic_data['Month'] = synthetic_data['Date'].dt.to_period('M').astype(str) # Group by 'Month' and calculate average sales actual_avg_monthly = salesDf.groupby('Month')['Sales'].mean().rename('Actual Average Sales') synthetic_avg_monthly = synthetic_data.groupby('Month')['Sales'].mean().rename('Synthetic Average Sales') # Merge the two series into a DataFrame avg_monthly_comparison = pd.concat([actual_avg_monthly, synthetic_avg_monthly], axis=1).fillna(0) # Plot plt.figure(figsize=(10, 6)) plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Actual Average Sales'], label='Actual Average Sales', marker='o') plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Synthetic Average Sales'], label='Synthetic Average Sales', marker='o') plt.title('Average Monthly Sales Comparison: Actual vs Synthetic') plt.xlabel('Month') plt.ylabel('Average Sales') plt.xticks(rotation=45) plt.grid(True) plt.legend() plt.ylim(bottom=0) # y-axis starts at 0 plt.tight_layout() plt.show() This chart also shows that the average monthly sales in both datasets are very similar, with only minimal differences. In this tutorial, we demonstrated how to prepare your data and metadata for synthetic data generation using the SDV library. By training a model on your original dataset, SDV can create high-quality synthetic data that closely mirrors the real data’s patterns and distributions. We also explored how to evaluate and visualize the synthetic data, confirming that key metrics like sales distributions and monthly trends remain consistent. Synthetic data offers a powerful way to overcome privacy and availability challenges while enabling robust data analysis and machine learning workflows. Check out the Notebook on GitHub. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP Server

Image: Daedalic EntertainmentEmbracer Group has seemingly been scrambling to right the ship after a turbulent few years, and its latest announcement reveals yet another significant move for the company. As reported by Game Developer, Embracer posted an investor statement to confirm that it is renaming its core gaming division from the working-title 'Middle Earth & Friends' to 'Fellowship Entertainment'. In addition, it's also looking to spin off the Coffee Stain Group into a "standalone group of community-driven game developers and publishers". The company notes that Fellowship Entertainment will have a total of approximately "6,000 employees across more than 30 countries". Here's a look at what it will be working on along with the teams it will encompass: "The group will be steward of the commercial rights to J.R.R. Tolkien’s work The Hobbit and The Lord of the Rings, as well as intellectual properties Kingdom Come Deliverance, Metro, Dead Island, Killing Floor, Darksiders, Remnant and Tomb Raider, amongst more than 300 other gaming IPs. "Fellowship Entertainment will consist of companies such as 4A Games, Aspyr Media, CrazyLabs, Crystal Dynamics, Dambuster Studios, Dark Horse, Deca Games, Eidos-Montréal, Flying Wild Hog, Gunfire Games, Limited Run Games, Middle-earth Enterprises, Milestone, PLAION, Tarsier Studios, THQ Nordic, Tripwire Interactive, Vertigo Games, and Warhorse Studios amongst more than 40 other companies." Meanwhile, Coffee Stain Group will consist of "more than 250 passionate game developers and publishers". We thought it might be fun to come up with our own alternatives to Fellowship Entertainment, so if you have any ideas yourself, be sure to let us know with a comment: Second Breakfast Games Tom Bombadeveloper One Does Not Simply Games You Shall Not Game Pass (sorry, Phil) Po-Tay-Toes Entertainment Sackville-Baggames It Comes in Pints Entertainment Sullied name Group discontinues all operations in Russia What do you make of this news from Embracer Group? Is this the turning point for the publisher? Let us know your thoughts. [source embracer.com, via gamedeveloper.com] Share:0 1 Nintendo Life’s resident horror fanatic, when he’s not knee-deep in Resident Evil and Silent Hill lore, Ollie likes to dive into a good horror book while nursing a lovely cup of tea. He also enjoys long walks and listens to everything from TOOL to Chuck Berry. Hold on there, you need to login to post a comment... Related Articles Xenoblade Dev Monolith Soft Is On The Hunt For New 2D And 3D Designers Recruitment sessions start next month Switch 2's Price May Pose "Challenges" For First-Year Sales, Says Furukawa Hence the lower-than-expected sales forecast Palworld Dev Announces "Disappointing" Game Changes Resulting From Nintendo's Lawsuit "We have had to make certain compromises" Nintendo Says Switch 2 Price Changes May Still Occur Depending On Tariffs Console price remains unchanged for now EA And Respawn Hit By Hundreds Of Layoffs As New Project Cancelled Between 300 to 400 people have lost their job