Exemplary Performance by NextBrain
NextBrain is an innovative no-code machine learning program that is revolutionizing the field of data science. NextBrain outperforms its competitors, such as Azure Machine Learning, Amazon SageMaker, BigML, Akkio, and obviously.ai, and delivers outstanding performance in terms of model accuracy, training time, and cost-effectiveness. By harnessing the power of artificial intelligence, NextBrain simplifies the process of creating and deploying ML models, making it accessible to businesses and individuals with little to no coding experience. With its intuitive user interface and powerful algorithmic capabilities, NextBrain is quickly becoming the go-to tool for those who demand accurate, efficient, and affordable ML solutions. You will learn how to build lightning-fast machine learning models for predicting several values such as ROI, weather data and customer churn.
We compared NextBrain to its competitors on six small to large-sized datasets. Those task included building both regression and classification models, and NextBrain achieved one of the most scalable, fastest, and accurate results. Let's dive into the numbers now!
(Note that the training time for AzureML isn't included as it takes around 20-30 minutes for AzureML to build a model, which isn't comparable to the speed of NextBrain)
The return on investment (ROI) is a crucial metric that evaluates the success of a marketing campaign. It is computed by dividing a campaign's net profit by its whole cost, and is typically stated as a percentage. ROI is regarded as a crucial indicator for marketing campaigns since it offers a means of determining a campaign's profitability and comparing it to other campaigns or investments. This can assist marketers in identifying the most successful initiatives and where to focus their future resources. ROI is significant since it aids in coordinating marketing initiatives with corporate goals.
Data Source: Dataslayer.ai
License: Data files © Dataslayer.aiLet's now upload ROI dataset, and predict MRR column:
As we can see, NextBrain was one of the only two platforms that could even create a model for ROI as the data insufficieny prevented other platforms from creating a machine learning algorithm. Moreover, NextBrain achieved a stunning 99% accuracy and placed at the top!
NextBrain was able to build a model in 8 seconds. The synthetic data option enables NextBrain to add artificially generated data to the original data and create results in a few seconds.
This dataset contains 32 features to predict the target column which determines if new Higgs bosons are produced, or the identical decay products but distinct kinematic features. Namely, this is a classifation problem example.
Data source: https://archive.ics.uci.edu/ml/datasets/higgs
License: Data files © Daniel Whiteson
NextBrain again accomplishes a competitive score with 82% accuracy. NextBrain's advanced algorithms and techniques can tackle various goals effectively.
Not only NextBrain achieved an astonishing accuracy result on the Higgs Boson dataset, we also still preserve our lightning speed claim by building a model in just 10 seconds.
Physics universal laws
In order to manage a number of challenging problems, modern data-driven systems for automated physics discovery must address the traditional problem of modeling falling objects of various sizes and masses. We want to solve the age-old issue of automatically discovering new physics by simulating falling objects of various sizes and masses. An substantial body of scientific research has been produced as a result of theoretical investigations into fluid forces on an idealized sphere. A ball's trajectory can also be altered by its spin via the Magnus force or lift force, which operates orthogonally to drag. This is in addition to gravity and drag. The air temperature, the wind, the elevation, and the form of the ball's surface are other factors that could influence the forces generated by a falling ball (de Silva et al. 2020).
Data Source: https://www.frontiersin.org/files/Articles/479363/frai-03-00025-HTML/image_m/frai-03-00025-t001.jpg
License: Data © Silva, B. M., Higdon, D. M., Brunton, S. L., Kutz, J. N
We are going to predict the 'Land Time' column:
Again, NextBrain proves its scalability by still being able to yield results for another challenging task with a marvelous 96% accuracy.
With its focus on generating reliable models in the breathtaking rapid way, NextBrain blows its competitiors out of the water. When it comes to training time, NextBrain isn't a joke.
Customer churn is the proportion of customers who discontinue utilizing a company's goods or services during a predetermined time frame. Because customer churn can have a major financial impact, it is crucial for a telecom business to track this indicator. To remain financially viable, telecom businesses must spread out their high fixed costs—such as the price of constructing and maintaining networks and infrastructure—across a sizable client base. The number of customers in general may fall if the customer turnover rate is high, which may make it more challenging for the business to cover its fixed expenditures. In the end, this can result in a reduction in profitability. A telecom business can take action to increase retention and lower the rate of churn by monitoring the customer churn rate in order to spot trends and patterns in customer behavior. This can be providing more affordable prices, enhancing customer support, or launching fresh, customer-friendly goods and services.
Data Source: Dataset adapted from IBM Business Analytics Community (https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113).
License: Data files © IBM# Final Remarks
We are going to predict the churn column which demonstrates whether a customer left within the last month.
NextBrain conserves its competing results by still hitting 80% accuracy, and becomes one of the top players.
NextBrain again demonstrates that it is undoubtedly a top contender in the auto-ML industry with its proven rapid model trainining results.
Many factors make weather forecasting crucial for communities. The ability to prepare for severe weather disasters like hurricanes, floods, and snowstorms is the primary way that accurate weather forecasts may help people stay safe. For instance, if a community is aware that a significant storm is approaching, it can take precautions to safeguard its residences, evacuate if needed, and store up on supplies. Accurate weather predictions are essential for economic reasons in addition to safety issues. The weather has an impact on many businesses and industries, and they depend on reliable forecasts to manage their operations and make wise decisions. For instance, farmers must consider the weather when deciding when to grow and harvest their crops, and construction firms must consider the weather when scheduling their workdays. Ultimately, reliable weather predictions are essential for daily life. They enable people to organize their outside activities, choose what to wear, and choose their mode of transportation. Although terrible weather can make individuals feel low or even harm their mental health, they may also have an effect on people's moods and general wellbeing.
Data Source: Adapted from Historical Hourly Weather Data 2012-2017 (https://www.kaggle.com/datasets/selfishgene/historical-hourly-weather-data?select=pressure.csv).
License: This dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
We are now going to predict the weather column, which is a categorical target:
Note that prediction weather data is still a prohibitively challenging tasks and as we can see, the accuracy results across all platforms can hardly hit 50%. NextBrain still manages to obtain 40% accuracy results.
Last but not the least, NextBrain again performs under 10 seconds.
The use of light detection and ranging (LIDAR) technology is now crucial for protecting the environment and is used to target an item or a surface and measure the time it takes for the light to reflect back to the receiver. It can be used, for instance, to create computer-generated 3-D images of regions on the surface of the Earth. As a result, ecologists and biologists have found the best partner in this technology for detection, classification, and conservation efforts. Nevertheless, processing the millions of data obtained for a single square meter of examination using this method demands a substantial amount of computational power. There are numerous mathematical and computational approaches that may be utilized to process all of these data points and generate readable and understandable results. All of these points need to be categorised and segmented. Three trees are represented by LIDAR in this dataset. The X, Y, and Z coordinates of each point serve as its representation. So, the dataset only comprises three input variables (x, y, and z) and a target (Tree).
Data Source: CIFOR and Wageningen University
License: Data files © CIFOR and Wageningen University# Final Remarks
NextBrain achieves a superb accuracy result on this dataset as well.
In this dataset, we used performance models on NextBrain rather than fast models, which took a slightly longer.
The Diabetes dataset is a well-known dataset used in machine learning and statistical analysis. It contains medical information for 768 patients, including demographic factors like age, gender, and body mass index (BMI), as well as clinical measures such as blood pressure and glucose level. The target variable is a binary classification indicating whether or not a patient developed diabetes within a five-year period. This dataset is commonly used for predicting the onset of diabetes using various machine learning algorithms.
Data Source: https://www.kaggle.com/datasets/mathchi/diabetes-data-set
License: CC0 Public Domain
NextBrain again blows its most challenging competitiors out of the water with a top-notch 98% accuracy on one of the most widely used datasets.
It takes less than one minute for NextBrain to achieve state-of-the-art results.
The Pricing strategy dataset is a dataset that includes information on pricing strategy for a fictional scooter company called 'Newco'. It contains data on various product features, such as sales prices, market share, brand awareness, and as well as safety, battery charging, and lifetime. This dataset is useful for analyzing the effectiveness of different pricing strategies and identifying which factors are most influential in determining the price of a product. It can be used to develop pricing models and to inform pricing decisions for similar products in the future.
Data Source: https://app.nextbrain.ai/model/8c084fe6?wp-name=Marketing+and+Sales&model-id=2af03e0b (NextBrain App, Marketing and Sales Workspace)
NextBrain is again one of the only two platforms out of the respective competitors that can build a model based on the given data. Its synthetic data option enables it to enrichen data to build accurate and fast machine learning models.
We also demonstrate all results below for a quicker comparison. NextBrain shines in its place across different datasets and model types.
NextBrain is one of the fastest auto-ml platforms in the industry.
In conclusion, NextBrain is a game-changing no-code machine learning program that sets itself apart from its competitors by delivering exceptional performance. With NextBrain, customers with no-coding experience can leverage the power of artificial intelligence to create and deploy accurate and efficient ML models with ease. Its intuitive interface, combined with advanced algorithms, allows businesses and individuals to streamline their ML operations while reducing costs and increasing productivity. Its state-of-the-art platform allows companies to automate the entire machine learning process, from data preprocessing to model training and deployment, resulting in highly accurate and efficient models. Overall, NextBrain is a must-have tool for anyone looking to stay ahead of the curve in the fast-evolving field of data science.
Updated on: 08/03/2023