What Is No Code Predictive Analytics? How To Build Using N8N, Google Colab, Akkio & Others?

Predictive analytics is a branch of data analytics that uses historical data, statistical algorithms, machine learning techniques to determine future outcomes, possibilities or trends.

Basically, it answers questions like: What is likely to happen next?

Here is a representation of data analytics (umbrella term):

Data Analytics = Umbrella term
├── Descriptive Analytics (What happened?)
├── Diagnostic Analytics (Why did it happen?)
├── Predictive Analytics (What is likely to happen?)
└── Prescriptive Analytics (What should we do about it?)

Predictive analytics exaggerate an extensive amount of coding and intellectual knowledge because it comprises software such as Python and RapidMiner, along with platforms like Azure ML and Amazon SageMaker.

But some platforms offer no code predictive analytics that predict future outcomes, customer buying behaviour, and sales predictions, all through visual tools and drag-and-drop interfaces, with zero coding.

TL;DR

• Predictive analytics helps forecast future outcomes using historical data and machine learning.
• No-code predictive analytics platforms let non-programmers build predictive models using drag-and-drop tools, no coding required.
• Popular tools like Akkio, Pecan, Obviously.AI, Google AutoML, and RapidMiner offer powerful forecasting, automation, and business insights at little to no cost.
• Key benefits include faster deployment, affordability, ease of use, and real-time data-driven decisions.
• Industries like e-commerce, healthcare, telecom, and marketing use these tools for tasks like fraud detection, churn prediction, and sales forecasting.
• The future is bright because No-code AI is expected to power 65% of business apps by 2026 (Gartner).

What Is No Code Predictive Analytics?

No code predictive analytics is a software that allows non-technical users (like business analysts, marketers, or product managers) to use of visual tools and drag-and-drop interfaces to build predictive models without writing any code.

Thoroughly, it is just like traditional predictive analytics, no-code analytics platform answers to the following.

• What will happen next?
• Which customers are likely to churn?
• How much sales will we make next month?

Users upload data (historical and current data), drag AutoML elements, and configure settings to produce outcome.

Also read: Novel AI Review: Is It The Best Story Writing AI Tool? (2024 Guide)

Key Aspects Of No Code Analytics Platforms

Various key aspects can be performed when utilize no code predictive analysis using ai tools, such as:

1. Accessibility

Within business, it makes predictive analytics accessible to a wider range of stakeholders such as internal teams, business users and shareholders , who may not have coding experience.

2. Visual Interface

The best thing about no code predictive ai tools is that they offer user-friendly, visual interfaces with drag-and-drop functionality to train models seamlessly.

3. Explicit Processes

With drag and drop AutoML templates, it automates many aspects of the predictive modeling process such as defining crucial objectives, collecting data, feature engineering, splitting data, model selection, model optimization (hyperparameter tuning), evaluating the model, and making predictions.

4. Quick Insights

Interestingly, no code predictive analytics offer lightning-fast insights on forecasting an array of tasks. By eliminating heavy coding tasks, businesses can get fast insights quickly, facilitating fast decision-making.

5. Real-Time Analytics

Lead scoring in real time, customer churn risk detection, present sales forecasting, live product recommendation, fraud detection in orders, and more are a few strong features of real-time no code predictive analytics.

Also read: Spotify User? Guide To Cancel Spotify Premium Subscription

5 Best Software Platforms Offer No Code Predictive Analytics

No code predictive analytics platforms, also referred to as no code machine learning platforms or no code ai tools, empower businesses to utilize AI and ML templates to predict the next outcome, decision, and goals.

#1. Akkio

Akkio is a user-friendly, no-code AI platform designed for business users without technical expertise. You upload data (CSV, Google Sheets, Excel), define your target variable (e.g. churn, conversion, revenue forecast), and Akkio automatically preprocesses, engineers features, selects the best algorithm, and trains a model.

Pros:

• Extremely intuitive interface; designed like spreadsheets.
• Auto‑ML handles feature engineering, algorithm selection, and training.
• Real‑time sync and batch prediction capabilities.

Cons:

• Limited customization of hyperparameters or model depth.
• Not built for advanced deep learning or very large datasets.

Suitable use cases: Leading scoring, churn detection, and demand forecast.

According to TrustRadius – “Akkio is widely adopted among SMBs and non‑technical teams and has earned strong user praise for ease of use and rapid ROI.”

# 2. Obviously AI

Obviously AI aims to democratize predictive analytics by allowing users to build ML models using natural-language prompts and drag‑and‑drop data upload. Being used by shoe-budget companies, it handles builds a predictive model instantly, displaying feature importance, what‑if analysis, and confidence scores, all without code.

Pros:

Super fast: models ready in minutes.
Beginner‑friendly as no ML knowledge required.
Offers what‑if simulations and API integration.

Cons:

Limited customization for model architecture or tuning.
Handles only small datasets effectively.

Suitable use cases: SMB marketing and sales performance forecasting.

According to IBR – “Obviously AI shines for lightweight predictive needs. It’s favored due to speed, simplicity, and low cost.”

#3. RapidMiner

RapidMiner is a dedicated drag‑and‑drop data science platform that enables visual ETL, feature engineering, modeling, evaluation, and deployment. It supports structured and unstructured data workflows and can be extended via R and Python scripts.

Pros:

• Comprehensive end-to-end predictive analytics platform.
• Visually support preprocessing, modeling, evaluation, and deployment.
• Rich library of algorithms and strong community support.

Cons:

• Resource-intensive and performance degrades on large or complex workflows.
• Require advanced learning which incurred extra cost.

Suitable use cases: Segmentation, prototyping, predictive maintenance, and supply chain modeling.

According to PeerSpot – “Used by over 400,000 professionals including large enterprises (BMW, Intel, Cisco), RapidMiner holds about 14.1% mindshare in the predictive analytics category in mid‑2025.”

#4. H2O.AI

H2O.ai Driverless AI is an enterprise-grade AutoML platform offering automatic feature engineering, algorithm selection, hyperparameter tuning, model explainability (via SHAP, partial dependence), and documentation generation.

Pros:

• Industry-leading in accuracy and automation.
• Built-in model explainability and compliance features.
• Scalable for large datasets and enterprise use.

Cons:

• Paid enterprise edition is expensive.
• Free versions have feature or usage restrictions.

Suitable use cases: Risk modeling in finance, healthcare outcome prediction, and retail pricing optimization.

According to Solution Review – “H2O.ai is consistently recognized by Gartner and PeerSpot as a top-tier AutoML tool in 2025, especially praised for automation and accuracy.”

#5. DataRobot AI Cloud

Highly web-powered, DataRobot AI Cloud provides full lifecycle enterprise AutoML with no-code predictive modeling, data ingestion, app building, deployment, monitoring, and governance. Built-in tools help model interpretability, decision flows, and compliance tracking.

Pros:

• Enterprise-grade scalability with model monitoring and governance.
• Supports model explainability, audit logs, and compliance.
• Decision app builder for interactive, no-code deployment.

Cons:

• Complexity may overwhelm non-technical users.
• Real-time deployment may require premium tiers.

Suitable use cases: Enterprise forecasting, explainable AI, and decision intelligence apps.

According to Wikipedia – “It’s frequently named a leader in AutoML by major analysts like Gartner due to its end-to-end pipeline and enterprise-friendly features.”

How To Build No-Code Predictive Analytics At Zero-Cost?

By using free-tier subscription or freemium services, relevant users can build no code predictive analytics at no extra cost.

1. Using Akkio (Fremium No-Code ML)

Let’s suppose you want to predict customer conversion. Use the following method to see in reality.

1. Sign up at akkio.com
2. Click “New Flow” → “Upload Data” (CSV, Excel, or Google Sheets).
3. Identify your target column (e.g., “Converted” or “Churned”).
4. Akkio auto-detects features → click Train.
5. After training, see feature importance and test with live data.
6. Use the Deploy tab, via Web API or other API service.

2. Using Google Colab + Gradio

Imagine you want forecast sales or classify sentiment using ready-made templates.

1. Go to Google Colab.
2. Search Sales prediction ML model.
3. Run notebook.
4. Export predictions to Google Sheets or download CSV.
5. Use public Gradio link to share predictive UI with team.

3. Using RapidMiner (Free Studio Desktop)

Let’s use case to predict employee attrition or customer churn visually.

1. Download RapidMiner Studio rapidminer.com/downloads.
2. Import your dataset (CSV, Excel, database).
3. Drag operators like Read CSV, Select Attributes, Logistic Regression, Decision Tree, and Apply Model.
4. Connect operators → Press Run.
5. Use “Performance” node to see accuracy, ROC, precision.
6. Save model → Re-use for new predictions.

4. Using Microsoft Power BI Desktop

How to use Power BI to forecast sales or trend prediction using Excel data.

1. Download Power BI Desktop powerbi.microsoft.com
2. Connect to Excel, CSV, or Google Sheet (via OneDrive).
3. Import your sales or time series data.
4. Create a Line Chart → Add a trend/forecast line.
5. Click “Analytics” pane → Enable “Forecast”.
6. Choose number of points to forecast (e.g., 3 months).
7. Apply filters for segmentation (by region, category).
8. Export dashboard or publish to PDF.

5. Using n8n + Google Sheets + Hugging Face

At last, let’s make, Auto-predict lead score based on form data on n8n.

1. Self-host or use free n8n instance (e.g., on Railway, Render).
2. Create a Google Sheet with columns: Name, Email, Industry, etc.
3. Set up n8n workflow by triggering Google Sheets→ New Row.
4. Use HTTP Request node → Connect to a public ML API.
5. Parse prediction and wait to see change in Google Sheets.

FAQs On No Code Analytics Platform

Who can use no-code predictive analytics?

Anyone with basic business data understanding for example, marketers, business analyst, eCommerce manager, HR professionals, and budgeted businesses can use no code predictive analytics.

How accurate are no-code predictive models?

Most platforms use AutoML to choose the best model. And there prediction scores are highly accurate. For example, platforms like Akkio or Pecan AI can achieve up to 85 to 95 percent accuracy.

What are the free no code analytics platform?

Many offer free tiers including Akkio, Obviously AI, and Google AutoML Tables with limited features.

How is no-code predictive analytics different from Generative BI?

Generative Business Intelligence (GenBI) focus on generating data visualizations, summaries, and reports using AI/NLP from existing data. Whereas, No-code AI Tools use machine learning to predict future outcomes.

Which industries benefit most from no-code predictive analytics?

Serious Retailers and eCommerce professionals are benefiting most from no code predictive analytics followed by financial infrastructure, healthcare businesses, marketing and logistics team.

Disclaimer: The information written on this article is for education purposes only. We do not own them or are not partnered to these websites. For more information, read our terms and conditions.

FYI: Explore more tips and tricks here. For more tech tips and quick solutions, follow our Facebook page, for AI-driven insights and guides, follow our LinkedIn page.