Graphite Note

Hello fellow sales experts! Do you think sales forecasting is just for data scientists? Think again!  💡 With the right tools and techniques, you too can effectively predict sales trends and drive better business decisions. Our latest guide will walk you through the steps to level up your forecasting game, making it accessible and actionable for sales professionals like you. Whether you’re looking to sharpen your skills or just get started, this guide has everything you need to forecast with confidence  💪 Check out the link in the comments section below! #SalesForecasting #SalesStrategy #DataDriven #SalesExperts #LevelUp #PredictiveAnalytics

Coming from a background in physics and astrophysics, I've always had a research-driven mindset. My journey has taken me from exploring the cosmos to diving deep into data science, and eventually, to building our no-code machine learning platform, Graphite Note But, I have to admit—research papers still hold a special place in my heart. 🤓So, imagine my excitement when I learned that Graphite Note was used in a recent research paper to create machine learning models supporting geological research! This study, focused on pollutant tracking in engineering geology in Ilorin, Kwara State, Nigeria, utilized our platform’s capabilities to analyze complex geological data. Seeing Graphite Note's screenshots and methodologies being applied in such a rigorous scientific context is a huge milestone for our young platform. This is not just a win for us but a testament to the versatility and power of no-code tools in advancing research across different fields. ❤️Kudos to my entire team for making this possible! Adeola Akinrinmade continuous success! Thrilled and looking forward to more applications like this. 🚀

🎉 Another happy client! Some positive words by Kevin Reid from Reid-Lewis Strategy Group who recently shared his experience with Graphite Note. His testimonial highlights how our tool has empowered his firm to offer more robust, data-driven recommendations without needing an in-house team of data scientists. We're always proud to see our clients succeed and even prouder to be a part of their journey! 🚀 #CustomerSuccess #DataDriven #GraphiteNote

Why Time Series Forecasting is a Fan Favorite on Graphite Note 💡One of the most popular models on our no-code AI platform, Graphite Note is the time series forecast—and I totally get why. Time series forecasting allows users to predict future values, whether it’s revenue, costs, product demand, or various KPIs. And the best part? You can do it all in just five minutes. Upload csv or connect to your db amd click magical ‘run model’ button. ▪️What makes this model so special is how it visualizes the fit of your predictions against actual data. You can instantly see whether your model is hitting the mark or needs some tweaking. ▪️Our clients are using time series forecasting across different industries, and the feedback has been amazing. They’re leveraging it to stay ahead of the curve, whether it’s in finance, retail, or supply chain management. 👾Honestly, I’ve always had a soft spot for time series forecasting. There’s something incredibly satisfying about being able to predict the future—at least when it comes to data! If you’re curious about how to make better predictions for your business, time series forecasting on Graphite Note might just become your new favorite tool too.

Both are superheroes, but they often get mistaken for one another! 😲 Presenting the epic showdown: Data Scientist vs. Decision Scientist -https://lnkd.in/eSjsECNv Deciphering between a Data Scientist and a Decision Scientist is no mere academic exercise – it's a strategic move. Our latest blog breaks down the difference, giving you the insight to make the right call! Read the full blog here: Data Scientist vs. Decision Scientist: https://lnkd.in/eSjsECNv #DataScience #DecisionScience #AnalyticsHeroes #GraphiteNote

As AI shapes our decisions, the need for transparency has never been more critical. Read now the importance of explainable AI (XAI) - https://lnkd.in/eU6UFiQa Sneak peek of what you can expect here! ➡ The crucial role of transparency: Discover why understanding AI's decision-making processes matters in critical areas, such as loan approvals and medical diagnoses. ➡ Building trust through clarity: Learn how xAI paves the way for trust and accountability in AI systems, enhancing reliability and ethical standards. ➡ Empowering collaboration: Explore how xAI fosters collaboration between humans and machines, leading to unprecedented efficiency and innovation. ➡ Practical applications across industries: From healthcare diagnostics to ensuring fairness in finance, see how xAI is making a tangible difference. ... and more! #ExplainableAI #TransparencyInAI #EthicalAI #GraphiteNote #Innovation #FutureOfAI

Think You’re Not Ready for Machine Learning? Think Again. I hear it all the time from folks: "We’re not ready for machine learning. We don’t have enough data. We don’t know where to start." Let me tell you, you’re more ready than you think, and getting started is a breeze. Starting Simple: You don’t need a mountain of data or a tech degree to dive into machine learning. With Graphite Note, we guide you through your use cases in a no-code environment. It's as easy as pie, and you can test your ideas in minutes. Just 30 Minutes: Seriously, give it a shot. Spend 30 minutes with us and you’ll know if you have enough data for machine learning. 👉Here’s how it works: Quick Setup: In a few clicks, you’ll have your first model up and running. Instant Feedback: We’ll show you if your model is strong or needs improvement. Clear Answers: Whether your model is good or needs work, you’ll know exactly where you stand. If it’s not there yet, you’ll know you need more data. If it’s solid, you can start using those predictions right away. 👉Why It’s Worth It: Clarity: You’ll get a clear answer about your readiness for machine learning, removing all the guesswork. Immediate Value: Even if your model isn’t perfect, you’ll gain valuable insights into your data, which is crucial for improvement. We’ve Got Your Back: At Graphite Note we’re here to help you every step of the way, making machine learning accessible and straightforward. Don’t let uncertainty hold you back. Machine learning isn’t some far-off, impossible goal. It’s a practical tool you can start using today. It’s 2024!

https://lnkd.in/eJyE8yjH - FACT: Every dollar counts! 🎯 So how do you ensure that your performance marketing budget goes the distance? The answer lies in predictive analytics. Our latest blog breaks down how you can leverage data and cutting-edge algorithms to allocate your marketing spend where it matters most. Here’s what you’ll uncover: 🔵 The Power of Predictive Analytics: Understand how extracting insights from historical data can forecast future trends and customer behaviors, meaning you can market smarter, not harder. 🔵 No-Code Solutions: You don't need a data scientist onboard! Solutions like Graphite Note put predictive analytics in your hands with zero coding involved. 🔵 Implementation strategies: Get practical tips on choosing the right tools, training your team, and measuring effectiveness to ensure your marketing budget is always an investment, not a cost. Learn more here: https://lnkd.in/eJyE8yjH #PredictiveAnalytics #MarketingROI #DataDriven #NoCode

💡Here is why I think every run of ML model adds value. Let’s debunk a common myth in machine learning: the idea that a model has to be perfect to provide value. Spoiler alert—it doesn’t! Even if your model isn’t performing at its best, there’s still significant value to be gained. 👉Here’s why: 1. Discovering Data Patterns: When you run a machine learning model, even a “crappy” one, you start to uncover patterns and insights in your data. These initial patterns can highlight areas where your data might be lacking or reveal hidden relationships you didn’t know existed. 2. Identifying Data Quality Issues: A poorly performing model often signals that there’s something off with your data. Maybe you have missing values, incorrect entries, or irrelevant features. By understanding where your model falls short, you can pinpoint and fix data quality issues, leading to more accurate future models. 3. Improving Feature Selection: Running a model helps you understand which features (or variables) are actually influencing your predictions. If your model isn’t performing well, it might be because you’re using the wrong features. This process of trial and error helps you refine your feature selection, honing in on the most relevant data points. 4. Iterative Improvement: Machine learning is inherently an iterative process. Each run of your model, even the bad ones, teaches you something new. You learn what doesn’t work, which is just as valuable as learning what does. This continuous learning loop is essential for building robust, accurate models. 5. Actionable Insights: Even with a basic model, you can start gaining actionable insights. For example, you might see trends or outliers that prompt further investigation. These insights can inform business decisions, guide strategy adjustments, and drive overall improvement. 6. Building Intuition: The more you experiment with models, the better your intuition becomes about your data and the problem at hand. This intuition is invaluable, guiding you toward more sophisticated models and better results over time. Real-World Example: At Graphite Note we’ve seen this firsthand. One of our clients initially struggled with a model predicting lead scoring and customer churn. The early models weren’t great, but they revealed crucial gaps in their data collection process. By addressing these gaps and iteratively refining their features, they eventually built a highly accurate model that significantly reduced churn rates. 💚Remember, the journey is just as important as the destination in machine learning. Each step, each model run, teaches you something valuable.