Welcome to Graphite Note App! To get started, create an account or log in if you already have one. With an active account, you can upload datasets and create machine learning models in just minutes.

Refer to our documentation to learn more about machine learning and how to apply ML models to your data to boost your business, uncover insights, and make predictions.

Welcome to the Graphite Note documentation portal. These guides will show you how to predict, visualize, and analyze your data using machine learning with no code. Graphite Note is a powerful tool designed to democratize the power of data analysis and machine learning, making it accessible to individuals and teams of all skill levels. Whether you're a marketer looking to segment your audience, a sales team predicting lead conversions, or an operations manager forecasting product demand, Graphite Note is your go-to platform.

The platform is built with a user-friendly interface that allows you to connect your data, generate predictive models, and share your results with just a few clicks. It's not just about making predictions; it's about understanding them. With Graphite Note's built-in prescriptive analytics and data storytelling feature, you can transform complex data into meaningful narratives that drive strategic actions. This documentation will guide you through every step of the process, from setting up your account to making your first prediction. So let's dive in and start exploring the power of no-code machine learning with Graphite Note.

For every lexical terms you can check on the .

The following sections cover some of the most important machine learning concepts.

In this section, we’ll explore the core machine learning concepts that underpin the Graphite Note solution. You’ll learn about the algorithms and techniques used to analyze data, make predictions, and uncover valuable insights. By understanding these foundational principles, you’ll gain a deeper appreciation of how Graphite Note leverages machine learning to deliver powerful analytical capabilities.

Oracle Connector for Graphite Note will soon be available for public use. This connector will enable seamless integration with Oracle databases, enhancing data accessibility and supporting advanced analysis within the Graphite Note platform. Stay tuned for its release to unlock efficient Oracle data connectivity.

The Logs Page in Graphite Note lets you review a complete history of model-training runs—showing start / finish times, durations, statuses, and model codes for quick auditing and troubleshooting. Use it to confirm that a model finished successfully, inspect failures, or copy a model-code for API calls. You can open the Logs Page by clicking the wheel icon (⚙️) in the top-right corner and selecting Logs.

For a full breakdown of every column and filter, see the Model Execution Logs page.

In Graphite Note, every user within a team is assigned a role that determines their access level and permissions. These roles help control what users can view or modify within the platform. Roles can be managed on the Roles Administration Page, accessed by clicking the wheel icon (⚙️) in the top-right corner and selecting Roles.

Role Types

By default in Graphite Note you will have two types of predefined roles that cover the most common access needs:

The Tags Page in Graphite Note is used to manage tags that help in organizing and distinguishing datasets, models, and notebooks. Tags improve searchability and allow users to categorize resources effectively. Note that only one tag can be assigned to each dataset, model, or notebook. The Tags Page can be accessed by clicking the wheel icon (⚙️) in the top-right corner and selecting Tags.

Tag List

Displays all created tags with their name, description, color, and a preview of how the tag appears.

Graphite Note is a no-code machine learning platform that enables data analysts to create predictive models in minutes. By connecting your data, the platform automatically preprocesses it, allowing you to train AI models without coding. It identifies key drivers affecting predictions and provides actionable insights through generative AI, facilitating data-driven decision-making. Graphite Note supports various industries with prebuilt model templates, making complex data analysis accessible and efficient.

Graphite Note offers three subscription plans tailored to different user needs: Sprout, Growth, and Enterprise. Each plan is designed to support various levels of analytics projects, from individual users to enterprise-scale teams. In addition to standard plans, Graphite Note offers add-ons that allow users to expand their capabilities based on specific needs.

To help you choose the best plan, you can start with a 14-day or with our data science experts.

For the latest details on pricing and plan features, please visit our official .

The "What Dataset Do I Need?" section of Graphite Note is a comprehensive resource designed to guide users through the intricacies of dataset selection and preparation for various machine learning models. This section is crucial for users, especially those without extensive AI expertise, as it provides clear, step-by-step instructions and examples on how to curate and structure data for different predictive analytics scenarios.

Key Features of the Section

1. Model-Specific Guidance: Each page within this section is tailored to a specific predictive model, such as cross-selling prediction, churn prediction, or customer segmentation. It outlines the type of data required, the format, and how to interpret and use the data effectively.

2. Sample Datasets and Templates: To make the process more user-friendly, the section includes sample datasets and templates. These examples showcase the necessary columns and data types, along with a brief explanation of each, helping users to model their datasets accurately.

3. Target Column Identification: A crucial aspect of preparing a dataset for machine learning is identifying the target column. This section provides clear guidance on selecting the appropriate target for different types of analyses, whether it's for classification, regression, or clustering.

4. Data Cleaning and Preparation Tips: Recognizing that data rarely comes in a ready-to-use format, this section offers valuable tips on cleaning and preparing data, ensuring that users start their predictive analytics journey on the right foot.

5. Real-World Applications and Use Cases: To bridge the gap between theory and practice, the section includes examples of real-world applications and use cases. This approach helps users understand how their data preparation efforts translate into actionable insights in various business contexts.

The main idea behind Graphite Notebook is to do your own Data Storytelling; create various visualization with detailed descriptions, plot model results for better understanding, etc.

In the next section, you’ll learn how to define a scenario, train a model, and leverage the results to make predictions, take strategic actions, and make data-driven decisions that directly impact your business.

Each model will be introduced on a dedicated page with step-by-step instructions and a video tutorial, guiding you through the process from setup to actionable insights.

You can access the Account information page by clicking on the small wheel icon (⚙️) in the top-right corner of Graphite Note, and then selecting the Account info drop-down item. This page features your account information, including active plan information and plan usege statistics along with information on different fatures included in the plan.

Assistance

A toggle option to enable assistance from the Graphite Note Support Team. By enabling this feature, users grant the support team access to their datasets, models, and notebooks. This facilitates quicker issue resolution and support for content creation. Assistance option is enabled by default.

Current Plan

Displays the subscription plan the account is currently on. Click on the Contact Sales button if you wish to upgrade, downgrade, or discuss your subscription options.

API Token

A secure token is shown (on the account info page it is partially masked for privacy) that users can use to integrate Graphite Note with other applications or systems. It provides a unique, secure key for API access, enabling advanced integration with external systems in a different ways:

• Dataset API - enables users to easily populate their datasets by sending data directly to Graphite Note, ensuring seamless data integration.

• Prediction API - allows users to request predictions based on attributes they provide, leveraging Graphite Note’s machine learning models to generate accurate business forecasts.

• Model Results API - lets users fetch the outputs of their trained models in a structured, paginated format. This is especially useful for viewing or processing large prediction result sets in batches.

Click the eye icon next to the token to view the full API token. More information about API Token usage you can find in the .

Plan usage

Gives a quick overview of your plan limits, usage, and enabled features:

• Max Data Rows in Database: Shows the total data rows allowed (e.g., 10M), how many are used (e.g., 2.28M), and how many are left (e.g., 7.72M). A progress bar helps you track usage.

• Max Number of Users: Displays the total users allowed (e.g., 10), how many are used (e.g., 1), and available slots (e.g., 9). You can invite new users with the Invite User link.

• Available Dataset Plans: Lists supported dataset types like CSV, database integrations, model data, merged datasets, and BigQuery.

• Additional Features: Confirms whether key features like AI insights, API access, white-labeled notebooks, and advanced model settings are enabled under your plan.

Introduction

In predictive modeling, key drivers (or influencers) are pivotal in discerning which features within a dataset most significantly impact the target variable. These influencers provide insights into the relative importance of each variable, enabling data scientists and analysts to understand and predict outcomes more accurately.

By highlighting the strongest predictors, key influencers inform the prioritization of features for model optimization, ensuring that models are precise and interpretable in real-world scenarios. This foundational understanding is crucial for refining models and aligning them closely with the underlying patterns and trends present in the data.

Reading Key Drivers

When examining the visualization of key influencers in Graphite Note Models, you'll find features arrayed according to their influence on the target variable, organized from most to least important on the left.

This ranking allows for a quick assessment of which factors are pivotal in the model's predictions.

By observing the length and direction of the bars associated with each feature, one can gauge the strength of influence they have on the target outcome.

Understanding Key driver influence

The image displays a visual breakdown of how different tenure values (the length of time a customer has stayed with a service) influence the likelihood of customer churn, specifically the outcome “Churn = Yes”.

The chart divides tenure into several ranges (or bins) and shows how each range increases or decreases the likelihood of churn compared to the average baseline. The churn likelihood is expressed as a multiplier:

• Customers with short tenure (1.93 to 7.58 months) are 2.18 times more likely to churn, the highest risk group in this analysis.

• Those in the 7.58 to 13.17 range also show an elevated risk, 1.46x more likely to churn.

• Churn likelihood continues to decrease with tenure — between 13.17 and 18.75 months, customers are 1.27x more likely to churn than average.

• In contrast, customers with tenure between 18.75 and 24.33 months are 1.04x less likely to churn — a marginal decrease, but moving into safer territory.

Statistical Methodology Used

Graphite Note uses a smart and transparent approach to show how each feature (or column) in your dataset influences the prediction outcome—like whether a customer is likely to churn. First, it calculates feature importance by checking how much the model relies on each column to make accurate predictions.

This is done using a method called permutation importance: the system shuffles the values of one feature and observes how much the model’s performance drops. The bigger the drop, the more important that feature is.

For numeric columns such as “tenure,” Graphite Note then goes one step further. It splits the values into logical ranges (called bins) and looks at how likely the predicted outcome (e.g., Churn = Yes) is for each range. It compares the average churn rate across the full dataset with the churn rate in each of those bins. If the churn rate in a bin is higher than average, it says the likelihood “increases” (e.g., by 2.18x); if it’s lower, it says it “decreases” (e.g., by 1.18x).

This lets you clearly see which value ranges increase or reduce the chances of a specific outcome. To keep insights relevant, the system automatically removes categories or ranges that don’t have enough data to be meaningful.

This makes the analysis not only accurate but also easy to act on—giving you clarity on what drives results and where to focus your strategy.

In machine learning, supervised and unsupervised learning are two main types of approaches:

1. Supervised Learning

In supervised learning, the model is trained on a labeled dataset. This means we provide both input data and the corresponding output labels to the model. The goal is for the model to learn the relationship between inputs and outputs so it can predict new, unseen data. Common examples include classification (e.g., email spam detection) and regression (e.g., predicting house prices).

For example, if you have an image dataset labeled with “cat” or “dog,” the model learns to classify new images as either a cat or dog based on this training.

Supervised learning on Diamonds dataset example

In this example, we have a dataset containing information about diamonds. The supervised machine learning approach focuses on predicting a specific target column based on other features in the dataset.

• Target is a Number (Regression): If the target column is numerical (e.g., “Price”), the goal is to predict the diamond’s price based on features like cut, color, and clarity. This is called regression.

• Target is Text (Classification): If the target is categorical (e.g., “Cut” with values like Ideal, Very Good), the goal is to classify diamonds into categories based on their characteristics. This is known as classification.

2. Unsupervised Learning

In unsupervised learning, the model is given only input data without any labeled outputs. The goal is to find patterns or groupings within the data. A common task here is clustering (e.g., grouping customers by purchasing behavior) and dimensionality reduction (e.g., simplifying data visualization).

For example, if you have images without labels, the model could group similar images together (like cats in one group and dogs in another) based on visual similarities.

Unsupervised learning on Diamonds dataset example

In unsupervised learning, there is no target column or labeled output provided. Instead, the model analyzes patterns within the data to group or cluster similar items together.

In this diamond dataset example:

• We don’t specify a target column (like price or cut); instead, the goal is to find natural groupings of data points based on their features.

• Here, clustering is used to identify groups of diamonds with similar Carat Weight and Price characteristics.

• The scatter plot on the right shows how the diamonds are grouped into different clusters (e.g., cluster0, cluster1, etc.), revealing patterns in the data without needing predefined labels.

This approach is useful when you want the model to identify hidden structures or patterns within the data. Unsupervised learning is often used for customer segmentation, anomaly detection, and recommendation systems.

Machine learning is a method that uses data to teach computers to recognize patterns and key drivers, allowing them to predict future outcomes without being explicitly programmed.

No-code machine learning is a simplified approach to machine learning that allows users to build, train, and deploy machine learning models without needing to write any code. This makes advanced data analysis accessible to non-technical users, empowering business teams to harness machine learning insights without relying on data scientists or programmers.

In no-code machine learning, platforms like Graphite Note provide intuitive interfaces where users can import data, select features, and train models through guided steps. For example, machine learning, as a method, uses data to teach computers to recognize patterns and key drivers, enabling them to predict future outcomes. In a no-code environment, this process is automated, allowing users to set up predictive models by simply uploading data and selecting key variables, all through a user-friendly, visual workflow.

By removing the complexity of coding, no-code machine learning enables organizations to leverage powerful data insights faster, supporting better business decisions and allowing companies to respond more quickly to market demands.

We have prepared 12 distinct demo datasets that you can upload and use in your Graphite Note account. These demo datasets include dummy data from a variety of business scenarios and serve as an excellent starting point for building and running your initial Graphite Note machine learning models. Instructions on how to proceed with demo dataset will be provided in the following pages.

You can access the Profile information setup by clicking on the small user profile icon located in the top-right corner of the interface. This icon typically displays the first letters of your name and surname (e.g., “CP” for Chris Parker). Once clicked, a dropdown menu appears where you can select Profile to view or edit your profile settings.

This page is where users can manage their personal information and customize their profile settings:

• User Code: A unique identifier for the user (e.g., a system-generated code like “e056ac433952”).

• Name: Displays the user’s name. This can be updated to reflect the preferred name.

• AI Generated Content Language: Lets the user select the language for AI-generated content (e.g., “English”). All AI generated content related to different models in Graphite note will be generate in selected language. When the language is changed, you need to rerun the model to generate content in the currently selected language.

• Select Avatar: Allows users to choose a personalized avatar color to visually represent their profile.

• Email: Shows the registered email address of the user.

• Role: Indicates the user’s role in the system (e.g., “Administrator” or “Viewer”).

• Password: Offers an option to change the user’s password with a Change Password button.

The Users Page in Graphite Note provides administrators with tools to manage team members, assign roles, and update user details. It can be accessed by clicking the wheel icon (⚙️) in the top-right corner and selecting Users.

User List

Displays all team members with details such as user code, name, AI content language, email, assigned role, and activation status. Administrators can manage users directly from this list.

Inviting New Users

New users can be invited by clicking on the Invite user button and entering their email address and assigning a role (e.g., Viewer, Administrator, or a custom role) from the Users Page. Once the invitation is sent, a user profile is automatically created within the system, even before the user accepts the invitation. This allows administrators to manage and edit the user’s details, such as their name, role, or preferences, immediately after the invitation is issued. The profile becomes fully active once the user accepts the invitation via email.

Editing Invited user

Once a user is invited, administrators can edit their details by clicking the gear icon under the Action column on the Users Page. This opens the Edit User Panel, where the following information can be found:

• User Code: A unique identifier for the user (non-editable).

• Name: Modify the user’s display name to ensure accuracy or reflect changes.

• AI Generated Content Language: Select the user’s preferred language for AI-generated content.

• Select Avatar: Customize the user’s profile avatar by choosing from a range of color options for better visual distinction.

• Email: Update the user’s registered email address.

• Role: Change the user’s assigned role (e.g., Viewer, Administrator, or a custom role) to adjust their access permissions.

Changes are saved by clicking the Save button, ensuring the user profile reflects updated details. This flexibility allows for seamless management of team members’ information and roles.

Expanding your dataset with more records can be beneficial for your machine learning model, but the impact depends on the quality and relevance of the new data. To learn how to re-upload or append data to your existing CSV dataset go here.

Here’s how adding more records might help:

1. Improved Generalization

More data generally helps the model to learn better and generalize to unseen data. If your initial dataset was limited, your model might have overfitted to the specific patterns in that data. Adding more data helps the model capture a wider range of patterns, leading to better performance on new data.

2. Reducing Overfitting

When your dataset is small, the model may learn noise or irrelevant patterns (overfitting). Expanding the dataset introduces more variety, making it harder for the model to memorize specific samples, thereby helping to reduce overfitting.

3. Better Representing the Data Distribution

A larger dataset often better represents the underlying data distribution, especially if the new records cover more edge cases, outliers, or scenarios that were underrepresented in the original dataset. This helps the model become more robust and perform well across a wider range of inputs.

4. Enhanced Model Accuracy

In most cases, expanding your dataset improves the accuracy of the model, especially if the model is data-hungry (like deep learning models). More data means more examples for the model to learn from, allowing it to better predict future outcomes.

5. Handling Class Imbalance

If your dataset suffers from class imbalance (e.g., if one class has far more records than another in a classification problem), adding more records from the minority class can make your dataset more balanced, improving the model’s ability to predict minority classes correctly.

Considerations:

• Quality over Quantity: Simply adding more data isn’t always beneficial if the additional data is noisy, irrelevant, or incorrectly labeled. High-quality, representative data is more important than just increasing the size of the dataset.

• Data Diversity: Adding data that captures a wider variety of features or scenarios is more helpful than adding redundant or very similar data points. If the new data points are too similar to the existing ones, the impact on model performance might be minimal.

• Graphite Note plan limits: Consider that expanding the dataset will increase both the computational requirements for training the model and the total number of dataset rows used in your current plan. More about Graphite Note plans finde .

Graphite Note offers a suite of powerful machine learning and advanced analytics models designed to empower businesses to make data-driven decisions efficiently. Each model is tailored to address specific business needs, from forecasting future trends to segmenting customer bases. With these models, users can transform raw data into actionable insights quickly and without the need for complex coding.

Here’s a quick introduction to each type of model:

1. Timeseries Forecast: Ideal for predicting future values in timeseries data, such as sales or demand, based on historical patterns and seasonality.

2. Binary Classification: Used to classify data into two distinct groups (e.g., yes/no or true/false) based on historical data patterns.

3. Multi-Class Classification: Expands classification to multiple categories, allowing predictions across several possible outcomes.

4. Regression: A model that predicts a continuous numeric value (e.g., sales amount or customer age) based on other input features.

5. General Segmentation: Unsupervised learning that groups similar entities together, helpful in creating customer or product segments based on numeric similarities.

6. RFM Customer Segmentation: A specialized segmentation technique that segments customers based on Recency, Frequency, and Monetary value, aiding in targeted marketing.

7. Customer Lifetime Value: Predicts the future value of customers, estimating metrics like repeat purchase date and overall customer value for better retention strategies.

8. New vs Returning Customers: Provides insights into customer behavior by segmenting new and returning customers over various time frames (daily, weekly, monthly, etc.).

9. Customer Cohort Analysis: Groups customers based on their first purchase date, allowing businesses to analyze behavior patterns over time.

10. ABC Analysis: Categorizes items into A, B, and C categories based on their impact on a chosen metric, helping prioritize resources on high-impact items.

Regressors help time series forecasting models “think ahead” by adding relevant external factors that influence the predicted outcome—such as promotions, price changes, or weather conditions. This makes your forecast more intelligent and better aligned with real-world scenarios.

📊 Key Takeaways from Our Case Study:

• Adding regressors to a sales forecast increased accuracy by 36%.

• Regressors helped the model understand why sales might spike or drop—beyond just seasonal patterns.

• They capture cause-effect relationships that time-based signals alone can’t fully explain.

• Results were visible immediately: better performance metrics like lower error rates (e.g., MAE, RMSE) and more accurate predictions during key events.

⚡ Before vs After Regressors

Below you can see a direct visual comparison of model performance before and after applying regressors:

✅ Why It Matter?

Regressors turn a simple forecast into a strategic tool. They give your model context about what’s happening in the business, allowing it to:

• Anticipate demand shifts more accurately

• Understand the impact of external drivers

• Provide more actionable insights for planning and decision-making

📖 Full Article

Read the full story and real-life example on our blog:

Dataset API enables users to easily populate their datasets by sending data directly to Graphite Note, ensuring seamless data integration.

Use this API to create new datasets directly in Graphite Note environment, specifying the dataset's structure. This API is particularly useful for automating the setup of datasets during the onboarding process, allowing for easy integration with client-specific data requirements.

In the following sections, you will find more details about the Dataset API.

There are plenty of free sources to find free datasets for machine learning.

Here is a list of some of the most popular ones.

For each dataset, it is necessary to determine its quality. Several characteristics describe high-quality data, but it is essential to point out accuracy, reliability, and completeness. Every high-quality data should be precise and error-free. Otherwise, your data is misleading and inefficient. If your data is not complete, it is harder to use because of the lack of information. What if your data is ambiguous or vague? You cannot trust your data; it's unreliable.

Free datasets for machine learning

By googling stuff like free datasets for machine learning, time-series dataset, classification dataset, etc., you see many links to different sources. But which of them includes high-quality data? We will list a few sources, but it is essential to know that among them, there are also data that have their drawbacks. Therefore, you have to be familiar with the characteristics of a good dataset.

Purpose

The Confusion Matrix is a powerful diagnostic tool in classification tasks within predictive analytics. It presents a clear and concise layout for evaluating the performance of a classification model by showing the actual versus predicted values in a tabular format. The matrix allows users, regardless of their coding expertise, to assess the accuracy and effectiveness of a predictive model, providing insights into not only the number of correct and incorrect predictions but also the type of errors made.

When you open a dataset, you have five different tabs: , , , , and tabs.

Settings

First, on the Settings tab, you can re-upload the dataset, rename it, and change the description and the tag. You also have the information on the type, the ID, the creation date, and the updated date.

Merging datasets allows you to combine data from different sources or related data for more comprehensive analysis.

Steps to Merge Datasets

Overview

Graphite Note’s CSV integration allows you to import your data from CSV files. This guide will walk you through the steps to import your CSV data into Graphite Note.

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

Analytics maturity represents an organization’s progression in leveraging data to drive insights and decisions. This journey typically follows four levels:

1. Descriptive Analytics: The foundation of analytics maturity, focused on answering “What happened?” Descriptive analytics relies on reporting and data mining to summarize past events. Most organizations begin here, gaining basic insights by understanding historical data.

2. Diagnostic Analytics: Building on descriptive insights, diagnostic analytics answers “Why did it happen?” by drilling deeper into data patterns and trends. Using techniques such as query drill-downs, diagnostic analytics provides context and explanations, helping organizations understand the causes of past events. Traditional organizations often operate within this descriptive and diagnostic phase.

3. Predictive Analytics: Moving into more advanced analytics, predictive analytics addresses “What will happen?” by utilizing machine learning and AI to forecast future outcomes. Through statistical simulations and data models, predictive analytics enables organizations to anticipate trends, customer behavior, and potential risks. Elevating to this level empowers organizations to make more proactive, data-driven decisions and gain a competitive edge.

In Graphite Note, data preparation is divided into two main steps to ensure optimal results, with all tasks handled automatically so you don’t have to worry about them. Data preprocessing is a crucial step in machine learning, enhancing model accuracy and performance by transforming and cleaning the raw data to remove inconsistencies, handle missing values, and scale features, and ensure compatibility with the chosen algorithm.

Step 1: Exclusion of Columns

Features Not Fit for Model: Graphite automatically excludes columns that aren’t suitable for modeling, such as date/datetime columns, to ensure only relevant features are used in training.

Data to Insights Lifecycle

• Dataset: Begin with a dataset containing historical data.

• Feature Selection:

Frequency Alignment: Matching Regressors to Your Model’s Time Granularity

Every regressor must supply exactly one value for each timestamp in your target series—no more, no less. Graphite Note enforces this 1:1 rule to ensure your external features line up perfectly with what you’re forecasting.

• Daily models → one regressor value per date

To create your notebook:

1. Go to Create New on Notebooks, or New Notebook when you are in the notebook list

1. Name your notebook. Additionally, you can add a description to your notebook. Also, you can select an existing (to connect your notebook with datasets and models) or create a new one.

If you've collected additional data related to your previously uploaded CSV file or there have been changes to the existing data, you can use the re-upload option. This allows you to update or append new data to your existing dataset. Expanding your dataset with more records can benefit your machine learning model, but the impact depends on the quality and relevance of the new data. Learn more about the benefits of dataset expansion .

How to Re-upload a CSV File:

1. Go to the Datasets List:

Description

The /dataset-complete endpoint is designed to signal the end of the dataset insertion process. Once all batches have been inserted via the /dataset-fill endpoint, this method should be called to trigger the final dataset shape calculation and any other necessary post-processing steps.

Parameters

What Are Regressors in Time Series Forecasting?

In machine learning, regressors (also called external regressors or exogenous variables) are additional features that help improve the accuracy of a model’s predictions. They represent outside influences—factors that may not be part of your target or time column, but that still affect the outcome you’re trying to forecast.

For time series models, regressors are often used to account for real-world conditions that can influence trends. These might include:

The Advanced ML Model Settings section in Graphite Note is designed for users who want to go beyond the basics. Whether you’re optimizing a forecast, enriching your model with external signals, or exploring deeper insights from your data, these advanced tools help you unlock the full potential of machine learning—without writing a single line of code.

This section covers:

• Enable strategic and feature-level AI-generated insights to guide real business decisions.

• Fine-tune internal model behavior (e.g., handling of outliers, binning strategies, or custom filters).

Graphite Note offers three powerful APIs to enhance your data-driven workflows. Whether you need to upload fresh data, generate predictions, or retrieve model outputs, these APIs are designed for seamless integration and rapid deployment

• -enables users to easily populate their datasets by sending data directly to Graphite Note, ensuring seamless data integration.

• -allows users to request predictions based on attributes they provide, leveraging Graphite Note’s machine learning models to generate accurate business forecasts.

• -lets users fetch the outputs of their trained models in a structured, paginated format. This is especially useful for viewing or processing large prediction result sets in batches.

Formatting Recommendations

1. Include a Header Row

• The first line should clearly list column names (e.g., customer_id, date, purchase_amount).

• Avoid using spaces or special characters in header names; if needed, use underscores or camelCase.

2. Check Delimiters

• Ensure the file uses a consistent delimiter (most commonly a comma , or semicolon ;).

• If your data contains commas within fields, enclose those fields in quotes (e.g., "123, Main Street").

3. Use UTF-8 Encoding

• Always save or export your file in UTF-8 format to avoid character encoding issues.

• This helps ensure Graphite Note can accurately parse all characters, including accented letters.

4. Date and Time Formats

• Use consistent date formats (e.g., YYYY-MM-DD) or a well-defined datetime format (e.g., YYYY-MM-DD HH:MM:SS).

• This helps Graphite Note accurately recognize date/time fields.

5. Handling Special Characters

• If your data contains quotes, commas, or other punctuation, ensure those fields are properly escaped or wrapped in quotes to prevent parsing errors.

6. Remove Unused Columns

• If possible, eliminate columns that you do not plan to analyze. This helps keep the CSV file size smaller and easier to handle.

7. Adhere to the 50 MB File Size Limit

• Graphite Note limits CSV uploads to 50 MB to ensure optimal performance and resource usage.

• If your file exceeds 50 MB, try splitting it into multiple CSVs or removing unnecessary columns and rows.

• For significantly larger datasets, consider connecting directly to a database (e.g., PostgreSQL, MySQL, BigQuery) to feed data into Graphite Note.

How to Create a CSV File from Excel?

1. Open Your Spreadsheet in Excel

• Make sure you review and clean up any unnecessary columns or rows before exporting.

2. Save As CSV

• Click on File → Save As (or Save a Copy in newer versions).

• Select CSV (Comma delimited) or CSV UTF-8 (Comma delimited) in the Save as type dropdown menu.

• Choose a file name and location, then click Save.

3. Verify Encoding

• If possible, choose CSV UTF-8 when saving.

• If CSV UTF-8 is not available, you may need to convert the file encoding separately (e.g., using a text editor like VSCode or Notepad++).

4. Confirm Data Integrity

• Open the resulting CSV file in a plain text editor to verify that the column names and any special characters appear correctly.

Overview

The MS SQL connector in Graphite Note allows you to import your data from a MS SQL or run custom SQL queries directly within the platform.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP address:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

• Option 2: From the datasets list, click on "New Dataset."

2. Select MS SQL

• Choose "MS SQL" as your dataset source and click "Next".

3. Enter Dataset Information

• Name: Provide a name for the dataset.

• Description: Add a short description of the data.

• Tags: Add tags for better organization.

• Click "Next" to proceed.

4. Establish a Connection

Fill in the following connection details:

• Server Name: Enter the hostname or IP address of your MS SQL instance.

• Database Port: Enter the port number for your database (typically 1433).

• Database User: Provide the username for the database.

• Database Password: Enter the password for the database user.

5. Check the Connection

• Click on "Check Connection" to validate the connection details.

6. Write and Run SQL

• Write the desired SQL query to fetch your data.

• Click on the "Run SQL" button to execute the query and retrieve the data.

7. Review and Adjust Data

• You should see all the columns from the selected dataset appearing.

• If necessary, you can change column names, data types, or data formats.

8. Create the Dataset

• Click on the "Create" button to finalize and create your dataset.

Troubleshooting Connection Issues

Ensure your firewall settings are configured to accept incoming requests from the IP address mentioned above. This is crucial for establishing a successful connection between Graphite Note and your MS SQL Server.

Import Process

Once the connection is validated:

• Graphite Note will initiate the data import process.

• The duration of this process depends on the size of your dataset. Small datasets will import in a few minutes, while larger datasets may take longer.

Next Steps

• Now that your data is imported and prepared, you can proceed to create a model without writing any code. Simply go to the and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create your machine learning model. In this case, we will select "Mall Customers dataset", to create General Segmentation analysis on customer engagement data.

3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.

4. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns, with their corresponding data types. Explore the dataset details on Summary tab.

5. To create a new model in the Graphite Note main menu, click on "Models"

6. You will get list of available models. Click on the "New Model" to create a new one.

7. Select the model type from our templates. In our case, we will select "General Segmentation" by double clicking on its name.

8. Select the dataset you want to use to produce model. We will use "Demo-Mall-Customers.csv"

9. Name your new model. We will call it "General Segmentation on Demo-Mall-Customers".

10. Write a description of the model and select a tag. If you want to, you can also create new tag from the pop-up "Tags" window that will appear on the screen.

11. Click "Create" to create your demo model environment.

12. To set up your General Segmentation model, firstly, you need to define "Feature" columns. That is numeric column (or columns) from your dataset on which segmentation would be based. In the case of General Segmentation on Mall Customers dataset, the numeric feature will be "Age", Annual Income" and "Spending Score" columns.

13. To start training the model click "Run Scenario". This will train your model based on the uploaded dataset.

14. Wait for few moments and Voilà! Your General Segmentation model is trained. Click on "Results" tab to get model insights and explore segmentation clusters.

15. Navigate over different tabs to get insights from high level "Cluster Summary" to "By Cluster" charts and tables.

16. Use "Cluster Visualisations" to view the scatter plot visualisations of cluster members.

Overview

The MySQL connector in Graphite Note allows you to import your data from a MySQL database or run custom SQL queries directly within the platform.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP address:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

• Option 2: From the datasets list, click on "New Dataset."

2. Select MySQL

• Choose "MySQL" as your dataset source and click "Next".

3. Enter Dataset Information

• Name: Provide a name for the dataset.

• Description: Add a short description of the data.

• Tags: Add tags for better organization.

• Click "Next" to proceed.

4. Establish a Connection

Fill in the following connection details:

• Server Name: Enter the hostname or IP address of your MySQL instance.

• Database Port: Enter the port number for your database (typically 3306).

• Database User: Provide the username for the database.

• Database Password: Enter the password for the database user.

5. Check the Connection

• Click on "Check Connection" to validate the connection details.

6. Write and Run SQL

• Write the desired SQL query to fetch your data.

• Click on the "Run SQL" button to execute the query and retrieve the data.

7. Review and Adjust Data

• You should see all the columns from the selected dataset appearing.

• If necessary, you can change column names, data types, or data formats.

8. Create the Dataset

• Click on the "Create" button to finalize and create your dataset.

Troubleshooting Connection Issues

Ensure your firewall settings are configured to accept incoming requests from the IP address mentioned above. This is crucial for establishing a successful connection between Graphite Note and your MySQL database.

Import Process

Once the connection is validated:

• Graphite Note will initiate the data import process.

• The duration of this process depends on the size of your dataset. Small datasets will import in a few minutes, while larger datasets may take longer.

Next Steps

• Now that your data is imported and prepared, you can proceed to create a model without writing any code. Simply go to the and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create your machine learning model. In this case, we will select MMM dataset to create Regression Analysis on marketing mix and sales data.

3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.

4. Adjust your dataset options on the Settings Tab. Click the Columns tab to view list of available columns with their corresponding data types. Explore dataset details on the Summary tab.

5. To create a new model in the Graphite Note main menu click on "Models".

Get an overview of Customer Churn demo dataset and how it can be used to create your new Graphite Note model in this video:

Or follow instructions below to get step by step guidance on how to use Customer Churn demo dataset:

1.If you want to use Graphite Note demo datasets click "Import DEMO Dataset"

2. Select the dataset you want to use to create a machine learning model. In this case we will select Churn dataset to create binary classification analysis on customer engagement data .

3. Once selected, demo dataset will load into your account. Dataset view will automatically open.

4. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create a machine learning model. In this case we will select "Ads" dataset to create a Regression Analysis on marketing ads data.

3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.

4. Adjust your dataset options on the Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.

5. To create a new model in the Graphite Note main menu click on "Models".

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select dataset you want to use to create your machine learning model. In this case, we will select Store Item Demand dataset to create Regression analysis on sales across store locations data.

3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.

4. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.

5. To create a new model in the Graphite Note main menu, click on "Models".

The Actionable Insights feature in Graphite Note is designed to provide users with tailored, data-driven recommendations generated using Generative AI. This functionality is available for models created with binary classification, multiclass classification, and regression tasks. Once model is trained, Graphite Note automatically generates two layers of insight designed for both strategic planning and data-driven operational decisions. To generate Actionable Insights in Graphite Note, you must enable them during the model scenario definition process.

These insights are available in two dedicated tabs:

• – executive-level narrative and high-level strategy

• – field-level exploration of key drivers and metrics

1. File Too Large

Symptom: You receive an error indicating that the file exceeds the size limit.

Cause: Graphite Note enforces a maximum 50 MB limit for CSV uploads.

Solution:

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select dataset you want to use to create your machine learning model. In this case we will select Upsell dataset to create binary classification analysis on additional purchases by customer data.

1. After selection, the demo dataset will automatically load into your account and the dataset view will open immediately.

4. Adjust your dataset options on the Settings tab. Click Columns tab to view the list of available columns with their corresponding data types. Explore dataset details on Summary tab.

Customer Lifetime Value (CLV) prediction is a process used by businesses to estimate the total value a customer will bring to the company over their entire relationship. This prediction helps in making informed decisions about marketing, sales, and customer service strategies.

Dataset Essentials for Customer Lifetime Value Prediction

A suitable dataset for CLV prediction should include:

• Date: The date of each transaction or interaction with the customer.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select a dataset you want to use to create your advanced analytics model. In this case, we will select Monthly Car Sales dataset to create a "Timeseries Forecast" analysis on car sales data.

3. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.

4. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.

5. To create a new model in the Graphite Note main menu, click on "Models".

Overview

The PostgreSQL connector in Graphite Note allows you to import your data from a PostgreSQL database or run custom SQL queries directly within the platform.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP addresses:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

Overview

The Execution Logs dialog (open via ⚙️ > Logs) records every model run across your workspace. It captures metadata such as start/end time, model type, hyper-parameters, and test-set metrics—providing a single place to verify, audit, and debug model training at scale.

Predict Revenue is a critical task for businesses aiming to forecast future revenue streams accurately. This challenge is typically addressed using a time series forecasting model, which analyzes historical revenue data to predict future trends and patterns.

Dataset Essentials for Predict Revenue

A suitable dataset for Predict Revenue using time series forecasting should include:

• Date/Time: The timestamp of revenue data, usually in daily, weekly, or monthly intervals.

• Revenue: The total revenue recorded in each time period.

• Seasonal Factors: Data on seasonal variations or events that might affect revenue.

• Economic Indicators: Relevant economic factors that could influence revenue trends.

• Marketing Spend: Information on marketing and advertising expenditures, if applicable.

An example dataset for Predict Revenue with time series forecasting might look like this:

Target Column: The Total Revenue column is the primary focus, as the model aims to forecast future values in this series.

Steps to Success with Graphite Note

1. Data Collection: Compile historical revenue data along with any relevant external factors.

2. Time Series Analysis: Utilize Graphite Note to analyze the time series data and identify patterns.

3. Model Training: Train a time series forecasting model using the platform.

Benefits of Predict Revenue with Time Series Forecasting

• Accurate Financial Planning: Enables more precise budgeting and financial planning.

• Strategic Decision Making: Informs strategic decisions with insights into future revenue trends.

• Adaptability to Market Changes: Helps businesses adapt strategies in response to predicted market changes.

In summary, Predict Revenue with time series forecasting is an essential tool for businesses to anticipate future revenue trends. Graphite Note simplifies this complex task, allowing businesses to leverage their historical data for insightful and actionable revenue predictions.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create the machine learning model. In this case, we will select CO2 Car Emissions dataset to create Regression Analysis on car emissions data.

3. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.

4. Adjust your dataset options on Settings tab. Click the Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.

5. To create a new model in the Graphite Note main menu click on "Models"

6. You will get list of available models. Click on "New Model" to create new one.

7. Select model type from our templates. In our case we will select "Regression" by double clicking on its name.

8. Select the dataset you want to use to produce the model. We will use "Demo-CO2-Car-Emissions-Canada.csv"

9. Name your new model. We will call it "Regression on Demo-CO2-Car-Emissions"

10. Write the model description and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.

11. Click "Create" to create your demo model environment.

12. To set up the Regression model first, you need to define "Target Feature". That is numeric column from your dataset that you'd like to make predictions about. In case of Regression on car emissions dataset target feature is "CO2 Emissions(g/km)" column.

13. Click "Next" to get the list of model features that will be included into scenario. Model relies upon each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.

14. To start training the model, click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.

15. Wait for few moments and Voilà! Your Regression model is trained. Click on the "Performance" tab to get model insights and view the Key Drivers.

16. Explore the Regression model by clicking on Impact Analysis and Training Results, to get more insights on how model is trained.

17. If you want to take your model into action, click on the "Predict" tab in the main model menu.

18. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset that the model will use to make predictions on the target column. In our case that is "CO2 Emissions (g/km)". Keep in mind, the dataset you are uploading needs to contain same feature columns as your model.

19. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select a dataset you want to use to create machine learning model. In this case we will select Housing-Prices dataset to create a "Regression Analysis" on house price historical data.

3. Once selected, the demo dataset will load directly to your account. The Dataset view will automatically open.

4. Adjust your dataset options on the Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.

5. To create a new model in the Graphite Note main menu click on "Models"

6. You will get list of available models. Click on a "New Model" to create new one.

7. Select the model type from our templates. In our case, we will select "Regression" by double clicking on its name.

8. Select the dataset you want to use to produce the model. We will use "Demo-Housing-Prices.csv"

9. Name your new model. We will call it "Regression on Demo-Housing-Prices"

10. Write the description of the model and select a tag. If you want to, you can also create new a tag from pop-up "Tags" window that will appear on the screen.

11. Click "Create" to create your demo model environment.

12. To set up a Regression Model, firstly, you need to define the "Target Feature". That is a numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Demo Housing Prices, the dataset target feature is "Price" column.

13. Click "Next" to get the list of model features that will be included into scenario. Model relies upon each column (feature) to make accurate predictions. When training model we will calculate which of the features are most important and behave as Key Drivers.

14. To start training the model, click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.

15. Wait for few moments and Voilà! Your Regression model is trained. Click on the "Performance" tab to get model insights and view the Key Drivers.

16. Explore the Regression Model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how model is trained and set up.

17. If you want to take your model into action. Click on "Predict" tab in the main model menu.

18. You can produce your own What-If analysis based on existing training results. You can also import fresh CSV dataset with data model will use to make predictions on the target column. In our case, that is "Price". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.

19. Use your model often to predict future behaviour, and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create machine learning model. In this case we will select eCommerce Orders dataset to create RFM Customer Segmentation (Recency, Frequency, Monetary Value) analysis on ecommerce orders data.

3. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.

4. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore the dataset details on Summary tab.

5. To create new model in the Graphite Note main menu click on "Models"

6. You will get list of available models. Click on "New Model" to create new one.

7. Select a model type from our templates. In our case we will select "RFM Customer Segmentation" by double clicking on its name.

8. Select dataset you want to use to produce model. We will use "Demo-eCommerce-Orders.csv".

9. Name your new model. We will call it "RFM customer segmentation on Demo-eCommerce-Orders".

10. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.

11. Click "Create" to create your demo model environment.

12. Click this text field.

13. To set up RFM model, you first need to identify and define few parameters. These are: "Time /Date Column", "Customer ID", "Customer Name" (optional) and "Monetary" (amount spent). In our case we will select "created_at" as date, "user_id" as customer and "total" as monetary parameter.

14. To start training model click "Run scenario".

15. Wait for a few moments and Voilà! Your RFM Customer Segmentation model is trained. Click on the "Results" tab to get model insights.

16. You can navigate over different tabs to get deep insights into RFM analysis from different perspectives: Recency, Frequency, Monetary.

17. Tab "RFM Scores" shows detailed explanation on different scores along with RFM segments and descriptions.

18. Tab "RFM Analysis" gives you more details on different segments

19. Tab "RFM Matrix" will show you number of customers belonging to different RFM segment. You can export matrix data to use Customer IDs for different business actions (e.g. exporting list of about to churn customers).

Overview

The Big Query connector in Graphite Note allows you to import your data from a BigQuery data warehouse.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP addresses:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

• Option 2: From the datasets list, click on "New Dataset."

2. Select Big Query

• Choose "Big Query" as your dataset source and click "Next".

3. Enter Dataset Information

• Name: Provide a name for the dataset.

• Description: Add a short description of the data.

• Tags: Add tags for better organization.

• Click "Next" to proceed.

4. Configure Big Query Connection

• Project ID: Enter your Google Cloud project ID.

• Dataset ID: Enter the Big Query dataset ID.

• Table ID: Enter the table ID if you want to import data from a specific table.

5. Download JSON Key from BigQuery

To enable Graphite Note to access your BigQuery data, you'll need to provide a service account key in JSON format:

1. Create a Service Account:

   • Navigate to the Google Cloud Console.

   • Select your project or create a new one.

   • Go to IAM & Admin > Service Accounts.

6. Upload JSON Key to Graphite Note

1. Access the Dataset Creation Page:

   • Return to the Graphite Note platform where you left off at the "Create a New Dataset" step.

2. Upload JSON Key:

   • You will be prompted to upload the JSON key file. Click on

7. Review data

Review the data and make any necessary adjustments:

• Rename Columns: Change column names if needed.

• Change Data Types: Adjust the data types of your columns as required.

• Click on the "Create" button to finalize and create your dataset.

Next Steps

Now that your data is imported and prepared, you can proceed to create a model without writing any code. Simply go to the and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

RFM Customer Segmentation: An Overview RFM (Recency, Frequency, Monetary) customer segmentation is a method businesses use to categorize customers based on their purchasing behavior. This approach helps personalize marketing strategies, improve customer engagement, and increase sales.

The segmentation is based on three criteria:

• Recency: How recently a customer made a purchase.

• Frequency: How often they make purchases.

• Monetary Value: How much money they spend.

Essential Dataset Components for RFM Segmentation A robust dataset for effective RFM segmentation includes the following key elements:

1. Date (Recency): The date of each customer's last transaction, essential for assessing the 'Recency' aspect of RFM.

2. Customer ID: A unique identifier for each customer, crucial for tracking individual purchasing behaviors.

3. Monetary Spent (Monetary Value): The total amount spent by the customer in each transaction, to evaluate the 'Monetary' component of RFM.

Example Dataset for RFM Customer Segmentation

Steps to Success with Graphite Note for RFM Segmentation

1. Data Collection: Gather comprehensive data including customer IDs, transaction dates, and amounts spent.

2. Data Analysis: Utilize Graphite Note to dissect the data, focusing on recency, frequency, and monetary values of customer transactions.

3. Segmentation Modeling: Employ models to segment customers based on RFM criteria, facilitating targeted marketing strategies.

Benefits of RFM Segmentation Using Graphite Note

• Enhanced Marketing Strategies: Tailor marketing campaigns based on customer segments.

• Improved Customer Engagement: Customize interactions based on individual customer behaviors.

• Efficient Resource Allocation: Focus efforts on the most profitable customer segments.

In conclusion, RFM Customer Segmentation is a powerful approach for businesses seeking to understand and cater to their customers more effectively. Graphite Note offers a no-code platform that simplifies the analysis of customer data for RFM segmentation, enabling businesses to leverage their data for strategic advantage in customer engagement and retention.

Introduction

Data labeling is the process of tagging data with meaningful and informative labels to train machine learning models. In predictive analytics, labeled data is crucial as it provides the model with examples of correct behavior. This document will guide you through the process of preparing and labeling data for three predictive models:

Lead Scoring,

Churn Prediction,

and MQL to SQL Conversion.

1. Lead Scoring Model (Converted: Yes/No)

Objective: Predict if a lead will convert into a customer.

Dataset Example:

Steps:

1. Data Collection: Gather data on leads, including their industry, company size, and interactions with your platform.

2. Labeling: For each lead, label them as 'Yes' if they converted into a customer and 'No' if they didn't.

3. Reasoning: Labeling helps the model understand patterns of conversion based on the features provided.

2. Churn Prediction Model (Churned: Yes/No)

Objective: Predict if a customer will churn or leave your service.

Dataset Example:

Steps:

1. Data Collection: Gather data on customer usage patterns, support interactions, and feedback scores.

2. Labeling: For each customer, label them as 'Yes' if they churned and 'No' if they continued using your service.

3. Reasoning: Labeling helps the model identify signs of customer dissatisfaction or reduced engagement, which might lead to churn.

3. MQL to SQL Conversion Model (Converted: Yes/No)

Objective: Predict if a Marketing Qualified Lead (MQL) will become a Sales Qualified Lead (SQL).

Dataset Example:

Steps:

1. Data Collection: Gather data on MQLs, including their engagement with webinars, content downloads, and email interactions.

2. Labeling: For each MQL, label them as 'Yes' if they became an SQL and 'No' if they didn't.

3. Reasoning: Labeling helps the model recognize patterns of engagement that indicate a lead's readiness to move to the sales stage.

Conclusion

Data labeling is a foundational step in predictive analytics. By providing clear, accurate labels, you enable your predictive models to learn from past data and make accurate future predictions. Ensure your labels are consistent and based on well-defined criteria to achieve the best results with Graphite Note's no-code predictive analytics platform.

Overview

The MariaDB connector in Graphite Note allows you to import your data from a MariaDB or run custom SQL queries directly within the platform.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP address:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

• Option 2: From the datasets list, click on "New Dataset."

2. Select MariaDB

• Choose "MariaDB" as your dataset source and click "Next".

3. Enter Dataset Information

• Name: Provide a name for the dataset.

• Description: Add a short description of the data.

• Tags: Add tags for better organization.

• Click "Next" to proceed.

4. Establish a Connection

Fill in the following connection details:

• Server Name: Enter the hostname or IP address of your MariaDB instance.

• Database Port: Enter the port number for your database (typically 3306).

• Database User: Provide the username for the database.

• Database Password: Enter the password for the database user.

5. Check the Connection

• Click on "Check Connection" to validate the connection details.

6. Write and Run SQL

• Write the desired SQL query to fetch your data.

• Click on the "Run SQL" button to execute the query and retrieve the data.

7. Review and Adjust Data

• You should see all the columns from the selected dataset appearing.

• If necessary, you can change column names, data types, or data formats.

8. Create the Dataset

• Click on the "Create" button to finalize and create your dataset.

Troubleshooting Connection Issues

Ensure your firewall settings are configured to accept incoming requests from the IP address mentioned above. This is crucial for establishing a successful connection between Graphite Note and your MariaDB Server.

Import Process

Once the connection is validated:

• Graphite Note will initiate the data import process.

• The duration of this process depends on the size of your dataset. Small datasets will import in a few minutes, while larger datasets may take longer.

Next Steps

• Now that your data is imported and prepared, you can proceed to create a model without writing any code. Simply go to the and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

Model Scenario

In this report, we want to divide customers into returning and new customers (this is the most fundamental type of customer segmentation). The new customers have made only one purchase from your business, while the returning ones have made more than one.

Let’s go through their basic characteristics.

New customers are:

• forming the foundation of your customer base

• telling you if your marketing campaigns are working (improving current offerings, what to add to your repertoire of products or services)

while returning customers are:

• giving you feedback on your business (if you have a high number of returning customers it suggests that customers are finding value in your products or service)

• saving you a lot of time, effort, and money.

Let's go through the New vs returning customer analysis inside Graphite. The dataset on which you will run your model must contain a time-related column.

Since the dataset contains data for a certain period, it's important to choose the aggregation level.

For example, if weekly aggregation is selected, Graphite will generate a new vs returning customers dataset with a weekly frequency.

It is necessary to contain data such as Customer ID

Additionally, if you want, you can choose the Monetary (amount spent) variable.

With Graphite, compare absolute figures and percentages, and learn how many customers you are currently retaining on a daily, weekly, or monthly basis.

Model Results

The model results consist of 4 tabs: , , , and Tab.

New vs Returning

Depending on the aggregation level, you can see the number of distinct and returning customers detected in the period on the New vs Returning Tab.

For example, in December 2020, there were a total of 2.88k customers, of which 1.84K were new and 1.05K returning. You can also choose a daily representation that is more precise.

Retention %

If you are interested in retention, the percentage of your returning customers, through a period, use the Retention % Tab.

Revenue New vs Returning

The results in the Revenue New vs Returning Tab depend on the Model Scenario: if you have selected a monetary variable in the , you can observe her behavior, depending on the new and returning customers.

Details

Last but not least, on the Details Tab, you can find a detailed table where you can see all relevant values which were used for the above results.

Overview

The Redshift connector in Graphite Note allows you to import your data from a Redshift database or run custom SQL queries directly within the platform.

Prerequisites

Before starting, ensure your firewall allows incoming requests from the following IP addresses:

• 99.81.63.220

Steps to Import Data

1. Create a New Dataset

• Option 1: Go to the homepage and click on "Create" under Datasets.

• Option 2: From the datasets list, click on "New Dataset."

2. Select Redshift

• Choose "Redshift" as your dataset source and click "Next".

3. Enter Dataset Information

• Name: Provide a name for the dataset.

• Description: Add a short description of the data.

• Tags: Add tags for better organization.

• Click "Next" to proceed.

4. Establish a Connection

Fill in the following connection details:

• Server Name: Enter the hostname or IP address of your Redshift instance.

• Database Port: Enter the port number for your database (typically 5432).

• Database User: Provide the username for the database.

• Database Password: Enter the password for the database user.

5. Check the Connection

• Click on "Check Connection" to validate the connection details.

6. Write and Run SQL

• Write the desired SQL query to fetch your data.

• Click on the "Run SQL" button to execute the query and retrieve the data.

7. Review and Adjust Data

• You should see all the columns from the selected dataset appearing.

• If necessary, you can change column names, data types, or data formats.

8. Create the Dataset

• Click on the "Create" button to finalize and create your dataset.

Troubleshooting Connection Issues

Ensure your firewall settings are configured to accept incoming requests from the IP addresses mentioned above. This is crucial for establishing a successful connection between Graphite Note and your Redshift data warehouse.

Import Process

Once the connection is validated:

• Graphite Note will initiate the data import process.

• The duration of this process depends on the size of your dataset. Small datasets will import in a few minutes, while larger datasets may take longer.

Next Steps

• Now that your data is imported and prepared, you can proceed to create a model without writing any code. Simply go to the and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

Model Scenario

Often companies spend a lot of time managing items/entities that have a low contribution to the profit margin. Every item/entity inside your shop does not have equal value - some of them cost more, some are used more frequently, and some are both. This is where the ABC Pareto analysis steps in, which helps companies to focus on the right items/entities.

ABC analysis is a classification method in which items/entities are divided into three categories, A, B, and C.

• Category A is typically the smallest category and consists of the most important items/entities ('the vital few'),

• while category C is the largest category and consists of least valuable items/entities ('the trivial many').

To create analysis you need to define 2 parameters:

• ID column This represents the unique identifier or description of each entity being analyzed, such as a product ID or product name.

• Numeric column - This is a measurable value used to categorize items into A, B, or C classes based on their relative importance. Common metrics include total sales volume, revenue, or usage frequency.

Model Results

Since ABC inventory analysis divides items into 3 categories, let's analyze these categories by checking the Model Results. The results consist of 4 tabs: , , , and Tabs.

Overview

In the Overview tab provides an actionable summary that supports data-driven decision-making by focusing on high-impact areas within the dataset. You’ll find a structured breakdown of entities within a chosen dimension (e.g., product_id) categorized based on a specific metric (e.g., price). This analysis highlights the contributions of different entities, focusing on the most impactful ones.

Key highlights in the Overview tab include:

• Category Breakdown: The dimension is divided into three categories:

• Category A: Top contributors representing few entities with a large share of the total metric.

• Category B: Mid-range contributors with moderate impact and growth potential.

• Category C: The largest group with the least individual impact.

• ABC Analysis Process: Explanation of sorting entities, calculating cumulative totals, and dynamically determining category boundaries based on cumulative contributions.

• Benefits and Next Steps: Highlights key points of the analysis. Encourages reviewing the Pareto Chart for visual insights, exploring detailed metrics, and identifying high-impact entities for strategic action.

ABC Summary

• The left chart shows the percentage of entities in each category (A, B, and C), illustrating how they are divided within the selected dimension (product_id).

• The right chart highlights each category’s contribution to the total metric (freight_price), showing how a smaller portion of entities (Category A) accounts for the majority of the impact, while the larger portion (Category C) has a lesser effect.

Together, these charts emphasize the purpose of ABC Analysis: to identify the “vital few” entities (Category A) that drive the most value, supporting targeted decision-making.

In the picture above, we can see that 33.77% of the items belong to category A and they represent 50.55% of the total value, meaning the biggest profit comes from the items in category A!

Pareto Chart

The , also called Pareto analysis, is based on the Pareto principle, which says that 80% of the results (output) come from 20% of the efforts (input). The Pareto Chart is a combination of a bar and a line graph - it contains both bars and lines, where each bar represents an item/entity in descending order, while the height of the bar represents the value of the item/entity. The curved orange line represents the cumulative percentage of the item/entity.

Details

The Details tab provides a granular view of the dataset resulting from the ABC Analysis. Each row represents an entity along with the following key details:

• The metric used for categorization, indicating each entity’s contribution (.

• The category assigned to each entity (A, B, or C) based on its relative impact.

• The cumulative percentage contribution of each entity to the total freight price, showing its share within the dataset.

This detailed breakdown allows users to identify specific high-impact entities in Category A, moderate contributors in Category B, and lower-impact entities in Category C, supporting data-driven prioritization and decision-making.

There is a long list of benefits from including ABC analysis in your business, such as improved inventory optimization and forecasting, reduced storage expenses, strategic pricing of the products, etc. With Graphite, all you have to do is upload your data, create the desired model, and explore the results.

Model Scenario

With General Segmentation, you can uncover hidden similarities in data, such as the relationship between product prices and customer purchase histories. This unsupervised algorithm groups data based on similarities among numerical variables.

To run this model in Graphite, first identify an ID column to distinguish between values (e.g., customers or products within groups). Next, select the numeric columns (features) from your dataset for segmentation.

Now comes the tricky part: data preprocessing! We rarely encounter high-quality data, so we must clean and transform it for optimal model results. What should you do with missing values? Either remove them or replace them with relevant values, such as the mean or a prediction.

For instance, if you have chosen Age and Height as numeric columns, Age might range between 10 and 80, while Height could range from 100 to 210. The algorithm could prioritize Height due to its higher values. To avoid this, you should transform/scale your data; consider standardizing or normalizing it.

In the end, you need to determine the number of groups you want to get. In case you are not sure, Graphite will try to determine the best number of groups. But what about the model result? More about that in the next post!

After reviewing all the steps, you can finish and Run Scenario. The training duration may vary depending on the data volume, typically ranging from 1 to 10 minutes. The training will utilize 80% of the data to train various machine learning models and the remaining 20% to test these models and calculate relevant scores. Once completed, you will receive information about the best model based on the F1 value and details about training time.

Model Results

Let's see how to interpret the results after we have run our model. The results consist of 5 tabs: , , , and Tabs.

Cluster Summary

As the model divided your data into clusters, a group of objects where objects in the same cluster are more similar to each other than to those in other clusters, it is essential to compare the average values ​​of the variables across all clusters. That's why in the Cluster Summary Tab you can see the differences between the clusters through the graph.

For example, in the picture above, you can see that customers in Cluster2 have the highest average value of the Total spend, unlike the customers in Cluster0.

By Cluster and By Numeric Value

Wouldn't it be interesting to explore each cluster by a numeric value or each numeric value by a cluster? That's why we have the By Cluster and By Numeric Value Tab - each variable and cluster are analyzed by their minimum and maximum, first and the third quartile, etc.

Cluster Visualization

You can also have a Cluster Visualization Tab that shows the link between two arguments and how they are distributed. You can change the measures to see different cluster and their distribution.

Details

Last but not least, on the Details Tab, you can find a detailed table where you can see all relevant values which were used for the above results.

With the right dataset and a few clicks, you will get results that will considerably help you in your business - general segmentation helps you in creating marketing and business strategies for each detected group. It's all up to you now, collect your data and start modeling.

What is Predictive Analytics?

Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast future activity, behavior, and trends. It involves applying statistical analysis techniques, analytical queries, and automated machine learning algorithms to data sets to create predictive models that place a numerical value — or score — on the likelihood of a particular event happening.

Media Mix Modeling (MMM) is a statistical analysis technique used to quantify the impact of various marketing channels on sales and other key performance indicators (KPIs). It helps businesses allocate their marketing budget more effectively by understanding the contribution of each channel to overall performance.

Dataset Essentials for Media Mix Modeling

A robust dataset for Media Mix Modeling should include:

• Time Period: The specific dates or periods for which the data is collected.

• Marketing Spend:

The Predict Cross Selling problem is a common challenge faced by businesses looking to maximize their sales opportunities by identifying additional products or services that a customer is likely to purchase. This predictive model falls under the multi-class classification category, where the objective is to predict the likelihood of a customer buying various products, based on their past purchasing behavior and other relevant data.

Dataset Essentials for Cross Selling

To effectively train a machine learning model for cross selling, you need a well-structured dataset that includes:

• Customer Demographics: Information like age, gender, and income, which can influence purchasing decisions.

Predicting customer churn is a critical challenge for businesses aiming to retain their customers and reduce turnover. This problem typically involves a binary classification model, where the goal is to predict whether a customer is likely to leave or discontinue their use of a service or product in the near future.

Dataset Essentials for Customer Churn Prediction

A well-structured dataset is key to accurately predicting customer churn. Essential data elements include:

• Customer Demographics: Age, gender, and other demographic factors that might influence customer loyalty.

Predictive Ads Performance is a process where businesses forecast the effectiveness of their advertising campaigns, particularly focusing on metrics like clicks, conversions, or engagement. This task typically involves regression or classification models, depending on the specific goals of the prediction.

Dataset Essentials for Predictive Ads Performance

A comprehensive dataset for Predictive Ads Performance focusing on predicting clicks should include:

• Date/Time: The timestamp for when the ad was run.

• Ad Characteristics:

Model Scenario

Do you want to understand how customer behavior changes over time, identify key trends, or measure the long-term impact of your business decisions? The Customer Cohort Analysis model in Graphite Note helps you do exactly that by grouping customers based on their first interaction or shared characteristics, and tracking their behavior over time.

A cohort is a group of customers who share a common attribute, most commonly the time of their first purchase. This model enables you to analyze how these cohorts perform over time, such as how often they return, how much they spend, and when their engagement drops off. This is a powerful tool for identifying growth opportunities, evaluating retention strategies, and improving customer lifetime value.

1. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".

2. Select the dataset you want to use to create machine learning model. In this case we will select a Diamonds dataset to create Multi Class Analysis on diamond characteristics data.

3. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.

4. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.

After you have created your notebook, we will go through some basic visualization tools (in case you missed how to create one, click ).

Data visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. This makes the data more natural for the human mind to comprehend, making it easier to identify trends, patterns, and outliers within large data sets.

Once you have created a notebook, to visualize we have to:

1. Select New visualization

Use this endpoint to define the columns, types, and other properties for a new dataset tailored to your needs.

Create a Dataset

To create a new dataset, follow these steps:

1. To create a new dataset, make a POST request to /dataset-create with the required parameters in the request body.

Even when a platform handles most of the heavy lifting, a little preparation and post-training care can unlock noticeably better predictions. Improving your machine learning models is a process. With better data, clear structure, and a few practical tweaks, you’ll get results that are not only accurate—but also actionable. Graphite Note is here to guide you every step of the way.

Start Simple: Use Default Settings

If you’re not familiar with advanced machine learning concepts, it’s best to leave the unchanged. The default settings in Graphite Note are optimized to give solid performance in most scenarios. You don’t need to tweak anything unless you’re confident about what each parameter does.