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 machine learning glossary.

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.

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

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.

What is Prescriptive Analytics?

Prescriptive analytics is a form of advanced analytics that examines data or content to answer the question "What should be done?" or "What can we do to make 'X' happen?". It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

What is AutoML?

AutoML (Automated Machine Learning) is the process of automating the end-to-end process of applying machine learning to real-world problems. It aims to make machine learning accessible to non-experts and to improve efficiency for experts by automating tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning.

How does Graphite Note ensure data security?

At Graphite Note, we take data security very seriously. We employ robust security measures to ensure your data is protected at all times. This includes encryption of data at rest and in transit, regular security audits, and strict access controls. Read more here.

What types of data can I use with Graphite Note?

Graphite Note is designed to work with a wide range of data types. You can import data from various sources such as CSV files, databases, data warehouses. The platform can handle structured, tabular data (like numerical and categorical data).

How can I import my data into Graphite Note?

Importing data into Graphite Note is a straightforward process. You can upload data directly from your computer, connect to a database, or your data warehouse. Our platform supports a variety of data formats, including CSV, and SQL databases.

What kind of support is available if I need help using Graphite Note?

We offer a range of support options to help you get the most out of Graphite Note. This includes a comprehensive knowledge base, video tutorials, and email support. Our dedicated support team is always ready to assist you with any questions or issues you may have.

Can I use Graphite Note if I don't have a background in data science?

Absolutely! Graphite Note is designed to be user-friendly and accessible to everyone, regardless of their technical background. Our no-code platform allows you to generate predictive and prescriptive analytics without needing to write a single line of code.

What industries can benefit from using Graphite Note?

Graphite Note is versatile and can be beneficial to a wide range of industries. This includes but is not limited to retail, e-commerce, marketing, sales, finance, healthcare, and manufacturing. Any industry that relies on data to make informed decisions can benefit from our platform.

How can Graphite Note help with my specific business needs?

Graphite Note is a flexible platform that can be tailored to meet your specific business needs. Whether you're looking to improve customer retention, optimize your marketing campaigns, forecast sales, or identify trends, our platform can provide the insights you need to drive growth.

What training resources are available for new users?

We offer a variety of resources to help new users get started with Graphite Note. This includes step-by-step tutorials, webinars, and a comprehensive knowledge base. We're committed to helping you get the most out of our platform and will work with you during onboarding.

How to update a model with new data?

You can easily re-upload a CSV file with Graphite Note here.

What is a tag?

A tag is a keyword associated with a model or dataset. It is a tool to group your models to easily find them, as you can filter your list by tags.

Also, you can create tags and manage them by clicking on the Account tab in the top-right of Graphite Note, and then the Tags drop-down item. You can change its name, its color, and its description or delete it.

You can also create a tag directly when you are importing a dataset or a model by clicking on Select tag and then Create & Apply or you can choose an existing one and Apply it.

What does parsing mean?

In programming, parsing refers to the process of analyzing and interpreting the structure of a sequence of characters or symbols according to a specific grammar or syntax. It is used in our application to understand and extract meaningful information from input data.

During parsing, a parser takes the input, which can be a program's source code or any other form of textual data, and breaks it down into a hierarchical structure that conforms to a predefined set of rules or grammar. This hierarchical structure is typically represented using a data structure such as an abstract syntax tree (AST) or a parse tree.

How does the free trial work?

Users can start their free trial of the SPROUT plan immediately. The trial is valid for the next 7 days. After seven days, if you want to continue our service, you must subscribe to a plan in communication with our sales team.

What's the difference between the starter (SPROUT) and other plans?

The starter plan is primarily designed for individual users that want to upload CSV files and create machine learning models.

The starter plan has the same core functionality as higher plans but with the following limitations:

• Only one user in the workspace.

• Only CSV connector

• Number of total data source rows limited to 50k

What is API access?

APIs enable you to pull your predictions into your ERP, CRM, internal app, or website. It is a way to process or display predictions outside your Graphite Note account.

What is a Dedicated Data Scientist?

With Graphite Note, you have the option to add a Dedicated Data Scientist to your team. This is an expert in machine learning and data science who can assist you and your team with any questions or concerns you may have. They can also provide hands-on support with tasks such as data cleaning and improving the performance of your models.

Can I extend my trial?

We can extend your trial beyond the one-week default period in certain circumstances. Don't hesitate to get in touch with us before the end of your trial if you'd like to discuss this further.

Special Discounts for Academia and Startups?

Our SaaS platform provides no-code predictive analytics, making data analysis accessible to everyone. As a company, we are dedicated to supporting the academic and startup communities and offer generous discounts to these groups.

If you want to hear more about our offerings, don’t hesitate to reach out to us!

Reputable Provider

Graphite Note runs on platforms belonging to reputable leading service providers and vendors that uphold the highest security standards, specifically: Amazon Web Service (AWS).

I want to know more about Data Science and Machine Learning lexical terms

For every lexical term you can check on the machine learning glossary.

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.

Components of the Confusion Matrix

A confusion matrix for a binary classification problem consists of four components:

• True Positives (TP): The number of instances that were predicted as positive and are actually positive.

• False Positives (FP): The number of instances that were predicted as positive but are actually negative.

• True Negatives (TN): The number of instances that were predicted as negative and are actually negative.

• False Negatives (FN): The number of instances that were predicted as negative but are actually positive.

Applications

In the context of Graphite Note, a no-code predictive analytics platform, the confusion matrix serves several key purposes:

1. Performance Measurement: It quantifies the performance of a classification model, offering a visual representation of the model's ability to correctly or incorrectly predict categories.

2. Error Analysis: By breaking down the types of errors (FP and FN), the matrix aids in understanding specific areas where the model may require improvement.

3. Decision Support: The confusion matrix supports decision-making by highlighting the balance between sensitivity (or recall) and precision, which can be crucial for business outcomes.

4. Model Tuning: Users can leverage the insights from the confusion matrix to adjust model parameters and thresholds to optimize for certain predictive behaviors.

5. Communication Tool: It acts as a straightforward communication tool for stakeholders to grasp the results of a classification model without delving into complex statistical jargon.

Interpretation

In the example confusion matrix (Model Performance -> Accuracy Overview)

• There are 799 instances where the model correctly predicted the positive class (TP).

• There are 15622 instances where the model incorrectly predicted the positive class (FP).

• There are 348 instances where the model failed to identify the positive class (FN).

• There are 18159 instances where the model correctly identified the negative class (TN).

The high number of FP and FN relative to TP suggests a potential imbalance or a need for model refinement to improve predictive accuracy.

Conclusion

The classification confusion matrix is an integral part of the model evaluation in Graphite Note, enabling users to make informed decisions about the deployment and iteration of their predictive models.

You can access to the Account information page by clicking on the Account tab in the top-right of Graphite Note, and then the Account info drop-down item.

This page features your personal information, including your team name, your current plan and the usage of it (data still available and the number of user). You can rename your team and contact sales to change your plan.

Each user in a team can have a role that you can see on the “users page” (more information on Team setup). There are two types of roles :

• Administrator: can read and modify entities

• Viewer: can only read entities, no editing allowed

The details are accessible through the Account tab in the top-right corner of Graphite Note, followed by selecting the Roles option from the drop-down menu.

If you want to change the properties of the viewer mode, you can click on the gear wheel to edit it.

You can now not allow access to some modules or give the possibility of editing them. You have also the possibility of deleting the role.

Data to Insights Lifecycle

• Dataset: Begin with a dataset containing historical data.

• Feature Selection: Identify the most important variables (features) for the model.

• Best Algorithm Search: Test different algorithms to find the best fit for your data.

• Model Generation: Create a predictive model based on selected features and the best algorithm.

• Model Tuning: Fine-tune the model’s parameters to improve accuracy.

• Model Deployment: Deploy the final model for real-world usage.

• Explore Key Drivers: Analyze the key factors influencing the model’s predictions.

• Explore What-If Scenarios: Test different hypothetical situations to see their impact.

• Predict Future Outcomes: Use the model to forecast future trends or outcomes.

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.

Welcome to Graphite Note App! To get started, or 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.

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.

4. Prescriptive Analytics: At the highest level of analytics maturity, prescriptive analytics answers “What should I do?” It combines machine learning, AI, and mathematical optimization to recommend actions that lead to desired outcomes. By offering actionable guidance, prescriptive analytics not only predicts future scenarios but also prescribes the best course of action, allowing organizations to optimize decisions and drive strategic growth.

While many organizations remain in the descriptive and diagnostic phases, those aiming to stay competitive and drive innovation must elevate their analytics capabilities. Graphite Note is designed to accelerate this journey, helping organizations seamlessly transition into predictive and prescriptive analytics. By embracing machine learning and AI through Graphite Note, companies can transform their data into a strategic asset, enabling proactive decision-making and unlocking new avenues for operational efficiency and business growth.

Designed for modern analytics teams and individuals, Graphite Note offers three different subscription plans to match various project preferences: Sprout, Growth and Enterprise.

To help you choose the best plan for your project, you can start with a 14 days free trial, schedule a demo, or book a meeting with our data science experts.

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.

In your , you can view the number of users allowed based on your plan. To invite users to your team, ensure you have available user slots and click on Invite user.

You will then be redirected to the “Users page” accessible through the Account tab in the top-right corner of Graphite Note, followed by selecting the Users option from the drop-down menu. On this page, you can see all the information on each user of your team.

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.

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!

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.

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 model type from our templates. In our case we will select "Binary Classification" by double clicking on its name.

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

9. Name your new model. We will call it "Binary Classification on Demo-Churn".

10. Write description of the model and select tag. If you want to, you can also create a 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 Binary Classification model first you need to define "Target Feature". That is binary column from your dataset that you'd like to make predictions about. In case of Binary Classification on Churn dataset, the target feature will be "Churn" column.

13. Click "Next" to get the list of model features that will be included in 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 model click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.

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

16. Explore Binary Classification model by clicking on Impact Analysis and Training Results to get more insights on how model is trained.

17. If you want to turn 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 a fresh CSV dataset with data model will use to make predictions on a target column. In our case that is "Churn". 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 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).

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.

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 a new one.

7. Select a model type from our templates. In our case we will select "Multi-Class Classification" by double clicking on its name.

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

9. Name your new model. We will call it "Multi-Class Classification on Demo-Diamonds"

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. To set up a Multi Class model first you need to define "Target Feature". That is text column from your dataset you'd like to make predictions about. In case of Multi Class on Diamonds dataset target feature is "Cut" column.

13. Click "Next" to get the list of model features that will be included under the scenario. Model relies on 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 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 "Performance" tab to get model insights and view Key Drivers.

16. Explore Multi Class model by clicking on Impact Analysis, Model Fit, Accuracy Overview or 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 a fresh CSV dataset to make predictions on target column. In our case that is "Cut". 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!

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.

The image shows a data visualization explaining how different amounts of interaction with website pages (measured in page visits) influence whether someone will take a specific action, labeled "Applied," with "YES" being the action taken.

1. For a high number of page visits, between 29.33 and 35, the likelihood of taking the action increases significantly—by more than double (2.26 times more likely).

2. For a moderate number of page visits, between 12.33 and 18, the action is still more likely but less so than the higher range—1.65 times more likely.

3. At a lower number of page visits, between 6.67 and 12.33, the action becomes less likely than the baseline by a factor of 1.37.

4. For very few page visits, less than 6.67, the likelihood of action drops drastically to less than half (2.36 times less likely).

The percentages and observations indicate how many cases fall within each range and how many of those cases resulted in the action "Applied" being taken. The visualization communicates that more engagement with the website (as measured by page visits) generally increases the likelihood of the desired action occurring.

Statistical Methodology Used

Graphite Note uses advanced statistical functions designed to calculate the influence of features on a target variable.

It employs a method of grouping the data by the feature and target columns and then counting occurrences. The calculations performed within this function aim to determine the proportion of each feature's categories contributing to a specific target value. The influence is quantified by comparing the observed proportion of the target value within each feature category against a weighted average, yielding an 'index value' that indicates the relative influence of each category on the target outcome. The function is robust, allowing for different data types in the target column, and ensures that only relevant categories with sufficient data are included in the final analysis.

Graphite Note here a quantitative analysis where numeric features (like 'Website Pages') are divided into bins or ranges.

The function then calculates the change in the likelihood of the target outcome (e.g., 'Applied' being 'YES') when the feature values fall within those bins. This calculation is done by comparing the base likelihood of the target outcome with the likelihood when the feature is within a specific bin, hence the multipliers like "increases by 2.26x" for certain ranges.

The analysis would remove any non-relevant categories (based on minimum percentage and row thresholds) and sort the results to clearly show which ranges of the feature increase or decrease the likelihood of the target outcome.

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".

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

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

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

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

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. To set up your "Regression Model", firstly, you need to define "Target Feature". That is numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Marketing Mix and Sales Dataset, the target feature is "Sales" column.

13. Click "Next" to get the list of model features that will be included in scenario. The 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 "Performance" tab to get model insights and view the Key Drivers.

16. Explore Regression model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how the 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 target column. In our case, that is "Sales". Keep in mind, 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 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.

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

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

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

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

9. Name your new model. We will call it "Binary Classification on Demo-Upsell".

10. Write a description of the model and select a 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 a Binary Classification model, firstly, you need to define "Target Feature". That is binary column from your dataset that you'd like to make predictions about. In case of Binary Classification on Upsell dataset target feature will be "Applied" column.

13. Click "Next" to get the list of model features that will be included in the model scenario. Your 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 your 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 Binary Classification model is trained. Click on "Performance" tab to get model insights and view the Key Drivers.

16. Explore Binary Classification 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 "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 to make predictions on the target column. In our case that is "Applied". 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!

Get an overview of Lead Scoring 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 Lead Scoring 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 the machine learning model. In this case, we will select "Lead Scoring dataset" to create binary classification analysis on potential customer interactions 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 the list of available columns with their corresponding data types. Then explore the dataset details on Summary tab.

5. Click "Models"

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

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

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

9. Name your new model. We will call it "Binary Classification on Demo-Lead-Scoring".

10. Write the description of the model and select a tag. If you want to, you can also create a 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 a Binary Classification model, firstly, you need to define the "Target Feature". That is a binary column from your dataset that you'd like to make predictions about. In the case of Binary Classification on a Lead Scoring dataset, the target feature will be the "Converted" column.

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

14. To start training 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 Binary Classification model is trained. Click on the "Performance" tab to get model insights and to view the Key Drivers.

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

17. If you want to turn 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 a fresh CSV dataset to make predictions on the target column. In our case that is "Converted". Keep in mind, 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 outcomes. The more you use and retrain your model, the smarter it becomes!

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.

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".

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

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

8. Select the dataset you want to use to produce the model. We will use "Demo-Monthly-Car-Sales.csv".

9. Name your new model. We will call it "Timeseries forecast on Demo-Monthly-Car-Sales".

10. Write description of the model and select a tag. If you want to, 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 Timeseries forecast analysis first you need to define the "Target Column". That is a numeric column from your dataset that you'd like to forecast. In the case of Timeseries on monthly car sales dataset target column is "Sales"

13. If dataset includes multiple time series sequences, you can select field that will be used to uniquely identify each sequence. In the case of our demo dataset, we will not apply Sequence Identifier field since we have only "Sales" target column.

14.

15. Click "Next" to open "Time/Date Column" selection. Choose "Month" as date column.

16. From additional options below, choose "Monthly" as time interval and define "Forecast Horizon". We will set up forecast horizon to 6 months in the future.

17. Click "Next" to activate "Seasonality" options step. Here, you can define seasonality specifics of your forecast. If time interval is set to daily on the next step you will also have "Advanced options" available.

18. Click "Run Scenario" to train your timeseries forecast.

19. Wait for a few moments and Voilà! Your Timeseries forecast is trained. Click on the "Performance" tab to get insights and view the graph with original(historical) and predicted model data.

20. Explore more details on "trend", "Seasonality" and "Details" tabs.

21. If you want to turn your model into action click on "Predict" tab in the main model menu.

22. You can produce your own Forecast analysis based on the existing training results by selecting Start and End date from drop down calendar and clicking on "Predict" button.

23. Use your model often to predict future sales results. 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 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".

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 model. We will use "Demo-Store-Item-Demand.csv".

9. Name your new model. We will call it "Regression on Demo-Store-Item-Demand".

10. Write a description of the model and select tag. If you want to, 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 Regression Model, firstly, you will need to define "Target Feature". That is the numeric column from your dataset that you'd like to make predictions about. In case of Regression on Store Item Demand dataset target feature is "Sales" column.

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

14. To start training your 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 "Performance" tab to get model insights and view Key Drivers.

16. Explore 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 a fresh CSV dataset to make predictions on target column. In our case that is "Sales". Keep in mind, 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 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".

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

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

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

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

10. Write the description of the model and select tag. If you want you can also create a 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 a "Regression Model", first you will 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 Ads dataset, target feature is "Clicks" column.

13. Click "Next" to get the list of model features that will be included in the 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 "Performance" tab to get model insights and view Key Drivers.

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

17. If you want to take turn 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 a fresh CSV dataset into the data model, to make predictions on target column. In our case that is "Clicks". 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!

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.

4. Model Evaluation: Continuously evaluate and adjust the model based on new data and changing market conditions.

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.

• User-Friendly Analytics: Graphite Note's no-code approach makes sophisticated time series forecasting accessible to users without specialized statistical knowledge.

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.

Product Demand Forecast is a crucial process for businesses to predict future demand for their products. This task typically involves time series forecasting models, which analyze historical sales data to forecast future demand patterns.

Dataset Essentials for Product Demand Forecast

An effective dataset for Product Demand Forecast using time series forecasting should include:

• Date/Time: The timestamp for each data point, typically daily, weekly, or monthly.

• Product Sales: The number of units sold or the sales volume of each product.

• Product Features: Characteristics of the product, such as category, price, or any special features.

• Promotional Activities: Data on any marketing or promotional activities that might affect sales.

• External Factors: Information on external factors like market trends, economic conditions, or seasonal events.

An example dataset for Product Demand Forecast might look like this:

Target Column: The Sales Volume column is the primary focus, as the model aims to forecast future sales volumes for each product.

Steps to Success with Graphite Note

1. Data Collection: Gather detailed sales data along with product features and external factors.

2. Time Series Analysis: Use Graphite Note to analyze the sales data over time, identifying trends and patterns.

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

4. Model Evaluation: Regularly evaluate the model's performance and adjust it based on new data and market changes.

Benefits of Product Demand Forecast

• Inventory Management: Helps in planning inventory levels to meet future demand, avoiding stockouts or overstock situations.

• Strategic Marketing: Informs marketing strategies by predicting when demand for certain products will increase.

• Resource Allocation: Assists in allocating resources efficiently based on predicted product demand.

• Accessible Forecasting: Graphite Note's no-code platform makes advanced forecasting techniques accessible to a wider range of users.

In summary, Product Demand Forecast is vital for businesses to anticipate market demand and plan accordingly. With Graphite Note, this complex analytical task becomes manageable, enabling businesses to leverage their data for effective demand planning and strategic decision-making.

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.

• Purchase History: Detailed records of past purchases, indicating which products a customer has bought.

• Engagement Metrics: Data on customer interactions with marketing campaigns, website visits, and other engagement indicators.

• Product Details: Information about the products, such as category, price, and any special features.

A typical dataset might look like this:

Target Column: The Target_Product column is crucial as it represents the product that the model will predict the customer is most likely to purchase next.

Steps to Success with Graphite Note

1. Data Collection: Gather comprehensive, clean, and well-structured data.

2. Feature Selection: Identify the most relevant features that could influence the model's predictions.

3. Model Training: Utilize Graphite Note's intuitive platform to train your multi-class classification model.

4. Evaluation and Iteration: Continuously assess and refine the model for better accuracy and relevance.

The Advantage of Predict Cross Selling

• Enhanced Customer Experience: By understanding customer preferences, businesses can offer more personalized recommendations.

• Increased Sales Opportunities: Identifying potential cross-sell products can significantly boost sales.

• Data-Driven Decision Making: Removes guesswork from marketing and sales strategies, relying on data-driven insights.

• Accessibility: With Graphite Note, even non-technical users can build and deploy these models, making advanced analytics accessible to all.

In conclusion, the Predict Cross Selling model is a powerful tool in the arsenal of any business looking to enhance its sales strategy. With Graphite Note, this complex task becomes manageable, allowing businesses to leverage their data for maximum impact.

Predictive Lead Scoring is a technique used to rank leads in terms of their likelihood to convert into customers. This approach typically employs a binary classification model, where each lead is classified as 'high potential' or 'low potential' based on various attributes and behaviors.

Dataset Essentials for Predictive Lead Scoring

To effectively implement Predictive Lead Scoring, a dataset with the following elements is essential:

• Lead Demographics: Information such as age, location, and job title.

• Engagement Metrics: Data on how the lead interacts with your business, like website visits, email opens, and download history.

• Lead Source: The origin of the lead, such as organic search, referrals, or marketing campaigns.

• Previous Interactions: History of past interactions, including calls, emails, or meetings.

• Purchase History: If applicable, details of past purchases or subscriptions.

An example dataset for Predictive Lead Scoring might look like this:

Target Column: The Converted column is the target variable. It indicates whether the lead converted to a customer.

Steps to Success with Graphite Note

1. Data Collection: Gather detailed and relevant data on leads.

2. Feature Selection: Choose the most predictive features for lead scoring.

3. Model Training: Utilize Graphite Note to train a binary classification model.

4. Model Evaluation: Test and refine the model for optimal performance.

Benefits of Predictive Lead Scoring

• Efficient Lead Management: Prioritize leads with the highest conversion potential, optimizing sales efforts.

• Personalized Engagement: Tailor interactions based on the lead's predicted preferences and potential.

• Resource Optimization: Allocate marketing and sales resources more effectively.

• Accessible Analytics: Graphite Note's no-code platform makes predictive lead scoring accessible to teams without deep technical expertise.

In summary, Predictive Lead Scoring is a powerful tool for optimizing sales and marketing strategies. With Graphite Note, businesses can leverage advanced analytics to score leads effectively, enhancing their conversion rates and overall efficiency.

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.

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 amount spent on each marketing channel during the period.

• Sales Data: The total sales achieved in the same time period.

• Channel Performance Metrics: Metrics like impressions, clicks, conversions, etc., for each channel.

• External Factors: Information on external factors like economic conditions, competitor activities, or seasonal events.

• Market Dynamics: Changes in market conditions, customer preferences, or product availability.

An example dataset for Media Mix Modeling might look like this:

Target Column: Totall Sales

Steps to Success with Graphite Note

1. Data Compilation: Gather comprehensive data across all marketing channels and corresponding sales data.

2. Model Development: Use Graphite Note, Regression Model, to develop a statistical model that correlates marketing spend across various channels with sales outcomes.

3. Analysis and Insights: Analyze the model's output to understand the effectiveness of each marketing channel.

4. Strategic Decision Making: Apply these insights to optimize future marketing spends and strategies.

Benefits of Media Mix Modeling

• Optimized Marketing Budget: Allocate marketing budgets more effectively across channels.

• ROI Analysis: Understand the return on investment for each marketing channel.

• Strategic Planning: Plan marketing strategies based on data-driven insights.

• Adaptability: Adjust marketing strategies in response to changing market conditions and consumer behaviors.

• Accessible Advanced Analytics: Graphite Note's no-code platform makes complex MMM accessible to teams without specialized statistical knowledge.

In summary, Media Mix Modeling is a powerful tool for businesses to optimize their marketing strategies based on comprehensive data analysis. With Graphite Note, this advanced capability becomes accessible, allowing for more informed and effective marketing budget allocation.

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.

• Strategic Business Decisions: Make informed choices regarding customer relationship management and retention strategies.

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.

Data is an essential component of any data modeling and analysis process. The kind of data you need for modeling depends on the specific problem you are trying to solve. In general, the data should be relevant, accurate, and consistent, and it should cover a significant period. In some cases, you may also need to preprocess or transform the data to make it suitable for modeling.

If you are new to using Graphite Note or are looking for some examples to practice with, there are several popular datasets available that you can explore. Some examples include weather data, financial data, social media data, and sensor data. These datasets are often available in open-source repositories or can be downloaded from public sources, such as government websites, social media platforms, or financial databases.

Graphite Note is a powerful tool that allows you to predict, visualize and analyze data in real-time. With the right dataset, you can use Graphite Note to gain valuable insights and make informed decisions about your business or research. Whether you are analyzing financial data to predict market trends or monitoring sensor data to optimize your production processes, our platform can help you make sense of your data and identify patterns that would be difficult to detect otherwise.

While the kind of data you need may vary depending on your specific needs, there are several popular datasets that you can use to practice and explore the capabilities of Graphite Note. With the right dataset and a solid understanding of data modeling and analysis, you can unlock the full potential of Graphite Note and gain insights that will drive your business or research forward.

We have highlighted a few popular datasets so you can get to know our platform better. After that, it's all up to you - collect your data and start having insights and fun!

An education company named “X Education” sells online courses to industry professionals. Many professionals interested in the courses land on their website and browse for courses on any given day—an excellent dataset for Binary Classification, with a target column "Converted" (YES/NO).

1.1 Usage

Use Graphite Note to gain valuable insights into your sales pipeline by identifying which leads are converting to customers and the factors that contribute to their success. With this information, you can optimize your sales strategy and improve your overall conversion rates.

In addition, our tool can also help you predict which new leads are most likely to convert to customers and provide a probability score for each lead. This can enable you to prioritize your sales efforts and focus on the leads with the highest conversion potential.

By leveraging our tool, you can gain a deeper understanding of your sales funnel and take proactive steps to improve your conversion rates, reduce churn, and increase revenue.

1.2 Model Type

1.3 How to Build This Model

To get started, download the provided dataset and upload it to Graphite Note. Once uploaded, create a new Binary Classification model in Graphite Note with the 'Converted' variable as the Target Variable. This will allow you to predict which leads are most likely to convert to customers.

After training the model, explore the insights that it provides, such as the most important features for predicting conversion and the distribution of conversion probabilities. This can help you to gain a better understanding of the factors that contribute to lead conversion and make informed decisions about your sales strategy.

1.4 What-if?

Finally, you can use the model to run a "what-if" scenario by predicting the conversion probability for new leads based on different scenarios or assumptions. This can help you to forecast the impact of changes in your sales approach or marketing efforts and make data-driven decisions.

By following these steps, you can leverage Graphite Note and the provided dataset to gain valuable insights into your sales pipeline, predict lead conversion, and optimize your sales strategy for better results.

A Telco company customer dataset. Each row represents a customer and each column contains the customer’s attributes. The dataset includes information about:

Customers who left the company – that will be our target column, ("Churn").

Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies.

Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges.

Demographic info about customers – gender, age range, and if they have partners and dependents.

2.1 Usage

Use Graphite Note to gain valuable insights into your customer base and identify which customers are most likely to churn. By analyzing the factors that contribute to churn, you can optimize your retention strategy and reduce customer churn rates.

In addition, our tool can also help you predict which customers are at high risk of churning, and provide a probability score for each customer. This can enable you to take proactive steps to retain those customers with the highest churn risk, such as offering personalized promotions or improving their overall experience.

By leveraging our tool, you can gain a deeper understanding of your customer base and identify opportunities to reduce churn, increase retention rates, and ultimately drive revenue growth. With our predictive churn model, you can make data-driven decisions that lead to more satisfied customers and a stronger business.

2.2 Model Type

2.3 How to Build This Model

To get started, download the provided dataset and upload it to Graphite Note. Once uploaded, create a new Binary Classification model in Graphite Note with the 'Churn' variable as the Target Variable.

This will allow you to predict which customers are most likely to churn.

After training the model, explore the insights that it provides, such as the most important features for predicting churn and the distribution of churn probabilities. This can help you to gain a better understanding of the factors that contribute to customer churn and make informed decisions about your retention strategy.

2.4 What-if?

Finally, you can use the model to run a "what-if" scenario by predicting the churn probability for different groups of customers based on different scenarios or assumptions. This can help you to forecast the impact of changes in your retention approach or customer experience efforts and make data-driven decisions.

By following these steps, you can leverage Graphite Note and the provided dataset to gain valuable insights into your customer base, predict customer churn, and optimize your retention strategy for better results.

The dataset contains monthly data on car sales from 1960 to 1968. It is great for our time series forecast model with which you can predict sales for the upcoming months.

3.1 Usage

Use Graphite Note to gain valuable insights into your business operations and forecast future trends by analyzing time series data. With our advanced forecasting models, you can make informed decisions about your business and optimize your operations for better results.

Our tool enables you to analyze historical data and identify patterns and trends, such as seasonality or cyclical trends. This can help you to forecast future demand or performance and make data-driven decisions about resource allocation, capacity planning, or inventory management.

3.2 Model Type

3.3 How to Build This Model

To get started, download the provided dataset and upload it to Graphite Note. Once uploaded, create a new Timeseries Forecast model in Graphite Note with

• The 'Sales' variable as aTarget Variable

• Time/Date Column: Month

• Time Interval: Monthly

After training the model, explore the insights that it provides, such as identifying patterns, seasonality, and trends. This can help you to forecast future performance, plan resources effectively, and optimize your operations.

3.4 What-if?

Finally, you can use the model to run a "what-if" scenario by predicting future values.

This can help you to forecast the impact of changes in your business operations, such as changes in demand, capacity planning, or inventory management.

By following these steps, you can leverage Graphite Note to gain valuable insights into your business trends, forecast future performance, and optimize your operations for better results. With our advanced time series forecasting models, you can stay ahead of the competition and take advantage of new opportunities as they arise.

This is a demo CSV with orders for an imaginary eCommerce shop. You can use it for Timeseries forecasting, RFM model, Customer Lifetime Value Model, General Segmentation, or New vs Returning Customers model in Graphite.

A demo Mall Customers dataset from Kaggle. Ideal for General customer segmentation in Graphite.

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.

• Usage Patterns: Data on how frequently and in what manner customers use the product or service.

• Customer Service Interactions: Records of customer support interactions, complaints, and resolutions.

• Transaction History: Details of customer purchases, payment methods, and transaction frequency.

• Engagement Metrics: Measures of customer engagement, such as email opens, website visits, or app usage.

A typical dataset for churn prediction might look like this:

Target Column: The Churned column is the target variable, indicating whether the customer has churned (Yes) or not (No).

Steps to Success with Graphite Note

1. Data Gathering: Collect comprehensive and relevant customer data.

2. Feature Engineering: Identify and create features that are most indicative of churn.

3. Model Training: Use Graphite Note to train a binary classification model on your dataset.

4. Model Evaluation: Test the model's performance and refine it for better accuracy.

Benefits of Predicting Customer Churn

• Proactive Customer Retention: Identifying at-risk customers allows businesses to take proactive steps to retain them.

• Improved Customer Experience: Insights from churn prediction can guide improvements in products and services.

• Cost Efficiency: Retaining existing customers is often more cost-effective than acquiring new ones.

• Accessible Analytics: Graphite Note's no-code platform makes predictive analytics accessible, enabling businesses of all sizes to leverage AI for customer retention.

In summary, the Predict Customer Churn model is an invaluable tool for businesses focused on customer retention. Through Graphite Note, this advanced predictive capability becomes accessible to businesses without the need for extensive technical expertise, allowing them to make informed, data-driven decisions for customer retention strategies.

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 here.

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.

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.

When you open a dataset, you have five different tabs: Settings, Columns, View Data, Summary, and Association 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.

Columns

The Columns tab provides you the original name, the column name, the data type, and the data format of each column, that you can modify.

View Data

On the View Data tab, you have all the data with the number of columns and rows.

Summary

The Summary gives a simple analysis of each column with a graph.

For numerical columns, it counts the number of null values; and calculates the sum, the mean, the standard deviation, the min, the max, the lower and upper quantile, and the median.

For categorical columns, it counts the number of null values, of unique values, and the min and max length.

Association

The last part is the Association tab, which measures the relationship between two variables. The association between numerical variables is the correlation:

• a zero correlation indicates no relationship between the variables

• a correlation of +1 indicates a perfect positive correlation, meaning that as one variable goes up, the other goes up as well

• a correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down

If you need, you can use the More details button to better understand associations.

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.

• Customer ID: A unique identifier for each customer.

• Monetary Spent: The amount of money spent by the customer on each transaction.

An example dataset for Customer Lifetime Value prediction might look like this:

Steps to Success with Graphite Note

1. Data Collection: Compile transactional data including customer IDs and the amount spent.

2. Data Analysis: Use Graphite Note to analyze the data, focusing on customer purchase patterns and frequency.

3. Model Training: Train a model to predict the lifetime value of a customer based on their transaction history.

Benefits of Predicting Customer Lifetime Value

• Targeted Marketing: Focus marketing efforts on high-value customers.

• Customer Segmentation: Segment customers based on their predicted lifetime value.

• Resource Allocation: Allocate resources more effectively by focusing on retaining high-value customers.

• Personalized Customer Experience: Tailor customer experiences based on their predicted value to the business.

• Strategic Decision-Making: Make informed decisions about customer acquisition and retention strategies.

In summary, predicting Customer Lifetime Value is crucial for businesses to understand the long-term value of their customers. Graphite Note facilitates this process by providing a no-code platform for analyzing customer data and predicting their lifetime value, enabling businesses to make data-driven decisions in customer relationship management.

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

Steps to Merge Datasets

1. Select Merge Dataset from the Menu

To begin the process, navigate to the main menu and select the "Merge Dataset" option. This will open a new window where you can start the merging process.

2. Enter Name and Description of the Merged Dataset

In the new window, you will see fields to enter the name and description of your new merged dataset. This helps you identify the purpose of the merged dataset for future reference. You can also add optional tags to further categorize your dataset.

3. Select Datasets to Merge and Define the Type of Join

Next, you will select the first dataset you want to merge from the dropdown menu. Repeat this step to select the second dataset.

After selecting your datasets, choose the type of join you want to perform: inner, left, right, or outer. The type of join determines how the datasets are combined based on the values in the key columns.

Then, select the key columns on which to merge the datasets. These are the columns that the datasets have in common and will be used to align the data.

4. Select Columns for Your New Merged Dataset

Now, you will choose which columns you want to include in your new merged dataset. You can select columns from either or both of the original datasets.

Once you've selected your columns, you can use the "Test This Merge" button to preview the merged rows. This allows you to check that the datasets are merging as expected before finalizing the process.

5. Create Your Merged Dataset

If you're happy with the preview of the merged dataset, click the "Create" button to finalize the merge. Your new merged dataset will now be available for use in your Graphite Note projects.

Remember, merging datasets is a powerful tool for combining and analyzing data in Graphite Note. By following these steps, you can easily merge datasets to gain new insights from your data.

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.

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 here.

How to Re-upload a CSV File:

1. Go to the Datasets List:

   • Navigate to the list of datasets.

1. Select the Dataset:

• Choose the dataset you wish to re-upload.

1. Select Re-upload:

• Click on the 'Re-upload' option.

1. Choose Options and upload:

• Decide on data append:

  • Depending on your needs, you can choose 'Append data' to add new records to the existing dataset. If 'Append data' is turned off, the new dataset will overwrite the existing

• Upload Your File:

  • Select or drag and drop your CSV file. Ensure the file has the same column structure as the previously uploaded file.

• Click Update to complete the re-upload process.

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.

• Database Name: Specify the name of the database you wish to connect to.

• SSL (Secure Sockets Layer): SSL is a protocol for encrypting information over the internet. If your MySQL instance requires SSL, ensure you enable this option.

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 Models and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

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.

• Database Name: Specify the name of the database you wish to connect to.

• SSL (Secure Sockets Layer): SSL is a protocol for encrypting information over the internet. If your MariaDB instance requires SSL, ensure you enable this option.

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 Models and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

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.

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

1. Select CSV file

• Choose "CSV" as your data source and click "Next".

1. 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.

1. Choose Parsing Options

• Configure your parsing options to properly interpret the CSV file.

  • Delimiter: Choose the character that separates values in your CSV (e.g., comma, semicolon).

  • Header: Indicate whether your CSV file has a header row.

  • Convert Empty Values to Null: Convert 'empty string' values to null. By default, this is turned on.

  • File Encoding: Select the file character encoding. The default is Unicode (UTF-8).

  • Skip Empty Rows: Skip empty rows at the beginning or end of the file. By default, Graphite Note will ignore those lines.

• Click on "Parse file" to process the file.

1. Review parsed data

   Review the parsed 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.

CSV filesize upload limits

Graphite Note limits the file size for CSV uploads to 50 MB. This restriction ensures that the platform maintains optimal performance, prevents excessive resource consumption, and ensures fast processing times for data uploads. If users need to work with larger datasets, alternative methods such as connecting directly to a database (Postgres, MySQL, BigQuery) can be used.

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 Models and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

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

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

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

2. Select PostgreSQL

• Choose "PostgreSQL" 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 PostgreSQL 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.

• Database Name: Specify the name of the database you wish to connect to.

• SSL (Secure Sockets Layer): SSL is a protocol for encrypting information over the internet. If your PostgreSQL instance requires SSL, ensure you enable this option.

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 PostgreSQL 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 Models and follow the steps to build and deploy your model using Graphite Note's intuitive interface.

Model Scenario

A Timeseries Forecast Model is designed to predict future values by analyzing historical time-related data. To utilize this model, your dataset must include both time-based and numerical columns. In this tutorial, we'll cover the fundamentals of the Model Scenario to help you achieve optimal results.

For the Target Column, select a numeric value you want to predict. It's crucial to have values by day, week, or year. If some dates are repeated, you can aggregate them by taking their sum, average, etc.

Next, you can choose a Sequence Identifier Field to group fields and generate an independent time series and forecast forecast for each group. Keep in mind, these values shouldn't be unique; they must form a series and there is maximum of 500 unique values allowed as sequence identifier. If you don't want to generate independent time series for each group, you can leave this option empty.

Then, select the Time/Date Column, specifying the column containing time-related values. The Time Interval represents the data frequency—choose daily for daily data, yearly for annual data, etc. With Forecast Horizon, decide how many days, weeks, or years you want to predict from the last date in your dataset.

The model performs well with seasonal data patterns. If your data shows a linear growth trend, select "additive" for Seasonality Mode; for exponential growth, select "multiplicative." For example, if you see annual patterns, set Yearly Seasonality to True. (TIP: Plotting your data beforehand can help you understand these patterns.) If you're unsure, the model will attempt to detect seasonality automatically.

For daily or hourly intervals, you can access Advanced Parameters to add special dates, weekends, holidays, or limit the target value.

Advanced Parameters

We are constantly enhancing our platform with new features and improving existing models. For your daily data, we've introduced some new capabilities that can significantly boost forecast accuracy. Now, you can limit your target predictions, remove outliers, and include country holidays and special events.

To set prediction limits, enter the minimum and maximum values for your target variable. For example, if you're predicting daily temperatures and know the maximum is 40°C, enter that value to prevent the model from predicting higher temperatures. This helps the model recognize the appropriate range of the Target Column. Additionally, you can use the Remove Days of the Week feature to exclude certain days from your predictions.

Country holidays and special dates

We added parameters for country holidays and special dates to improve model accuracy. Large deviations can occur around holidays, where stores see more customers than usual. By informing the model about these holidays, you can achieve more balanced and accurate predictions. To add holidays in Graphite Note, navigate to the advanced section of the Model Scenario and select the relevant country or countries.

Similarly, you can add promotions or events that affect your data by enabling Add special dates option. Enter the promotion name, start date, duration, and future dates. This ensures the model accounts for these events in future predictions.

Combining these parameters provides more accurate results. The more information the model receives, the better the predictions.

Removing data points

In addition to adding holidays and special events, you can delete specific data points from your dataset. In Graphite Note, enter the start and end dates of the period you want to remove. For single-day periods, enter the same start and end date. You can remove multiple periods if necessary. Understanding your data and identifying outliers or irrelevant periods is crucial for accurate predictions. Removing these dates can help eliminate biases and improve model accuracy.

By following these steps, you can harness the full potential of your Timeseries Forecast Model, providing valuable insights and more accurate predictions for your business. Now it's your turn to do some modeling and explore your results!

Training model

After setting all parameters it is time to Run Scenario and train Machine Learning model.

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 Performance

Model Fit

The Model Fit Tab displays a graph with actual and predicted values. The primary prediction is shown with a yellow line, and the uncertainty interval is illustrated with a yellow shaded area. This visualization helps assess the model's performance.

If you used the Sequence Identifier Field, you can choose which value to analyze in each Model Result.

Trend

Trends and seasonality are key characteristics of time-series data that should be analyzed. The Trend Tab displays a graph illustrating the global trend that Graphite Note has detected from your historical data.

Seasonality

Seasonality represents the repeating patterns or cycles of behavior over time. Depending on your Time Interval, you can find one or two graphs in the Seasonality Tab. For daily data, one graph shows weekly patterns, while the other shows yearly patterns. For weekly and monthly data, the graph highlights recurring patterns throughout the year.

Special Dates

The Special Dates graph shows the percentage effects of the special dates and holidays in historical and future data.

Details

Details tab shows the results of the predictive model, presented in a table format. Each record includes the predicted label, predicted probability, and predicted correctness, offering insights into the model's predictions, confidence, and accuracy for each data point. Dataset test results can be exporetd into Excel by clicking on the XLSX button in the right corner.

Take actions with Timeseries forecast

Once the model is trained, you can use it to predict future values, solve binary classification problems, and drive business decisions. Here are ways to take action with your Timeseries forecast model:

Predict

After building and analyzing a predictive model using Graphite Note, the "Predict" function allows you to apply the model to new data. This enables you to forecast outcomes or target variables based on different feature combinations, providing actionable insights for decision-making.

Create Notebook

You can share your prediction results with your team using the Notebook feature. With Notebooks, users can also run their own predictions on your Timerseries Forecast model.

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.

Model Scenario

With the Multiclass Classification model, you can analyze the importance of the features with 2-25 distinct values. Unlike binary classification, which deals with only two classes, multiclass classification handles multiple classes simultaneously.

To achieve the best results, we will cover the basics of the Model Scenario. In this scenario, you choose parameters related to the dataset and the model.

To run the model, you need to select a Target Feature first. This target is the variable or outcome that the model aims to predict or estimate. The Target Feature should be a text-type column (not a numerical or binary column).

You will be taken to the next step where you can choose all the Model Features you want to analyze. You can select which features the model will analyze. Graphite Note will automatically exclude some features that are not suitable for the model and will provide reasons for each exclusion.

Moving forward, you'll see a comprehensive list of preprocessing steps that Graphite Note will apply to prepare your data for training. This enhances data quality, ensuring your model produces accurate results. Typically, these steps are performed by data scientists, but with our no-code machine learning platform, Graphite Note handles it for you. After reviewing the preprocessing 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 Performance

Key Drivers

Key Drivers indicate the importance of each column (feature) for the Model's predictions. The higher the reliance of the model on a feature, the more critical it is. Graphite uses permutation feature importance to determine these values.

Impact Analysis

The Impact Analysis tab allows you to select various features and analyze, using a bar chart, how changes in each feature affect the target feature. You can switch between Count and Percentage views.

Model Fit

The Model Fit Tab displays the performance of the trained model. It includes a stacked bar chart with percentages showing correct and incorrect predictions for multiclass feature.

Accuracy Overview

On the Accuracy Overview tab, you'll find detailed information on correct and incorrect predictions (True positives and negatives / False positives and negatives). Model metrics are explained at the bottom of the section.

Training Results

In the Training Results Tab, you will find information about all the models automatically considered during the training process. Graphite ran several machine learning algorithms suitable for multiclass classification problems, using 80% of the data for training and 20% for testing. The best model, based on the F1 score, is chosen and marked in green in the models list.

Details

Details tab shows the results of the predictive model, presented in a table format. Each record includes the predicted label, predicted probability, and predicted correctness, offering insights into the model's predictions, confidence, and accuracy for each data point. Dataset test results can be exported into Excel by clicking the XLSX button in the right corner.

Take actions with Multiclass Classification

Once the model is trained, you can use it to predict future values, solve multi-class classification problems, and drive business decisions. Here are ways to take action with your Multiclass Classification model:

Actionable Insights

In Graphite Note, you can generate Actionable Insights using the Actionable Insights Input Form. Here, you can provide specific details about your business and objectives. This data is then combined with model training results (e.g., Multiclass Classification with Key Drivers) to produce a tailored analytics narrative aligned with your goals.

Actionable Insights leverage generative AI models to deliver these results. These insights are conclusions drawn from data that can be directly turned into actions or responses. You can access

Actionable Insights from the main navigation menu, provided you are subscribed to a Graphite Note plan that includes actionable insights queries.

Predict

After building and analyzing a predictive model using Graphite Note, the Predict function allows you to apply the model to new data. This enables you to forecast outcomes or target variables based on different feature combinations, providing actionable insights for decision-making.

Create Notebook

You can share your prediction results with your team using the Notebook feature. With Notebooks, users can also run their own predictions on your Multiclass Classification model.

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.

   • Click on + CREATE SERVICE ACCOUNT.

   • Provide a name and description for the service account, then click CREATE.

2. Grant Permissions:

   • Assign the necessary roles, such as BigQuery Data Viewer and BigQuery Job User. These roles allow the service account to view datasets and execute queries.

3. Create Key:

   • In the service account list, click on the created service account.

   • Go to the Keys tab and click ADD KEY > Create new key.

   • Select JSON as the key type and click Create. This will download the JSON key file to your computer.

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 Upload JSON Key and select the file you downloaded from the Google Cloud Console.

3. Check Connection:

   • After uploading the JSON key, click Check Connection to ensure that Graphite Note can successfully connect to your BigQuery instance.

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

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.

• Database Name: Specify the name of the database you wish to connect to.

• SSL (Secure Sockets Layer): SSL is a protocol for encrypting information over the internet. If your MS SQL instance requires SSL, ensure you enable this option.

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

Model Scenario

A regression model in machine learning is a type of predictive model used to estimate the relationship between a dependent variable (target feature) and one or more independent variables. It aims to predict continuous outcomes by fitting a line or curve to the data points, minimizing the difference between observed and predicted values. To get the best possible results, we will go through the basics of the Model Scenario. In Model Scenario, you select parameters related to the dataset and model.

To run the model, you have to choose a Target Feature first. The target refers to the variable or outcome that the model aims to predict or estimate. In this case, it should be a numerical column.

You will be taken to the next step where you can choose all the Model Features you want to analyze. You can select which features the model will analyze. Graphite Note will automatically exclude some features that are not suitable for the model and will provide reasons for each exclusion.

Moving forward, you'll see a comprehensive list of preprocessing steps that Graphite Note will apply to prepare your data for training. This enhances data quality, ensuring your model produces accurate results. Typically, these steps are performed by data scientists, but with our no-code machine learning platform, Graphite Note handles it for you. After reviewing the preprocessing 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 Performance

Key Drivers

Key Drivers indicate the importance of each column (feature) for the Model's predictions. The higher the reliance of the model on a feature, the more critical it is. Graphite uses permutation feature importance to determine these values.

Impact Analysis

The Impact Analysis tab allows you to select various features and analyze, using a bar chart, how changes in each feature affect the target feature. You can switch between Count and Percentage views.

Model Fit

The Model Fit Tab displays the performance of the trained model. It includes a stacked bar chart with percentages showing comparison between known outcomes (historical) and model predicted outcomes.

Training Results

In the Training Results Tab, you will find information about all the models automatically considered during the training process. Graphite ran several machine learning algorithms suitable for multiclass classification problems, using 80% of the data for training and 20% for testing. The best model, based on the F1 score, is chosen and marked in green in the models list.

Details

The Details tab shows the results of the predictive model, presented in a table format. Each record includes the predicted label, predicted probability, and predicted correctness, offering insights into the model's predictions, confidence, and accuracy for each data point. Dataset test results can be exported into Excel by clicking on the XLSX button in the right corner.

Take actions with Multiclass Classification

Once the model is trained, you can use it to predict future values, solve multi-class classification problems, and drive business decisions. Here are ways to take action with your Regression model:

Actionable Insights

In Graphite Note, you can generate Actionable Insights using the Actionable Insights Input Form. Here, you can provide specific details about your business and objectives. This data is then combined with model training results (e.g., Regression model training results) to produce a tailored analytics narrative aligned with your goals.

Actionable Insights leverage generative AI models to deliver these results. These insights are conclusions drawn from data that can be directly turned into actions or responses. You can access

Actionable Insights from the main navigation menu, provided you are subscribed to a Graphite Note plan that includes actionable insights queries.

Predict

After building and analyzing a predictive model using Graphite Note, the Predict function allows you to apply the model to new data. This enables you to forecast outcomes or target variables based on different feature combinations, providing actionable insights for decision-making.

Create Notebook

You can share your prediction results with your team using the Notebook feature. With Notebooks, users can also run their own predictions on your Regression model.

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

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.