How to Use Microsoft Fabric Notebooks for Accurate Sales Forecasting
Microsoft Fabric Notebooks help you manage Sales Forecasting. You follow easy steps to do this. First, you get your data ready. Next, you look at the data. Then, you build a model. After that, you check how well it works. Last, you make charts to show your results. This way, you use real tools and new data science ideas. You can work with your team and see what happens together. Here are some things businesses often get:
By doing these steps, your business can make smarter choices and get better results.
Key Takeaways
Get your data ready with care. Make sure it is clean and neat. This helps you get good sales forecasts.
Look at your data to find patterns and odd points. Doing this makes your model work better.
Make your forecasting model in Microsoft Fabric Notebooks. Work with your team at the same time to get better results.
Check how well your model works using things like MAE and RMSE. This makes sure your forecasts can be trusted.
Show your results in a clear way. Use charts and dashboards to help your team understand the information.
Sales Forecasting Workflow
When you start with Sales Forecasting in Microsoft Fabric Notebooks, you follow a clear path. This path helps you move from raw data to a working model that predicts future sales. Let’s walk through each step together.
Data Preparation
You begin by gathering your sales data. Most people use actual sales numbers, forecast data, year-end forecasts, and monthly or daily sales values. You can bring this data into Microsoft Fabric from many sources. Once you have your data, you store it in a central place called Lakehouse Storage. This makes it easy to find and use later.
Cleaning your data is very important. Here are some best practices you can follow:
Add comments to your code so you remember why you made certain changes.
Write cleaning steps as functions. This way, you can use them again in other notebooks.
Check your data after cleaning to make sure it looks right.
Keep your raw data safe. Save cleaned data in a new table or view.
Use Data Wrangler to explore and clean your data before you write custom code.
Watch how long your cleaning steps take, especially with big datasets.
Clean your data in small batches if you have a lot of it.
Tip: Always keep your raw data. You might need to go back and check it later.
Here’s a quick look at how data science techniques help you prepare your data in Fabric:
Exploratory Analysis
Now, you want to understand your data better. You use exploratory data analysis (EDA) to spot trends, patterns, and any problems. In Microsoft Fabric Notebooks, you can use different techniques to do this:
You can also use tools in Fabric to find outliers. Outliers are numbers that look very different from the rest. You might see them when you make a histogram or check for missing values. Power BI can help you spot these outliers with easy-to-read charts.
Note: Outliers can affect your Sales Forecasting results. Always check for them before you build your model.
Model Building
After you know your data, you can start building your Sales Forecasting model. Here’s a simple workflow you can follow in Microsoft Fabric Notebooks:
Load your cleaned sales data into the notebook.
Explore the data again to make sure it is ready.
Train a machine learning model using open-source packages like scikit-learn or PySpark ML.
Track your experiments. This helps you remember which settings worked best.
Save your final model so you can use it to make predictions later.
You can work with your team in real time. Everyone can see the code, results, and charts as you build your model. This makes it easy to share ideas and improve your Sales Forecasting process together.
Working in notebooks lets you test ideas quickly. You can try different models and see which one gives the best results.
By following these steps, you set up a strong foundation for accurate Sales Forecasting. You use data science tools, work with your team, and make sure your data is clean and ready. This workflow helps you get the most out of your sales data and make better business decisions.
Model Evaluation and Prediction
After you make your model, you need to see if it works well. You also use it to guess future sales. This step helps you trust your results. It lets you make smart business choices. There are two main parts: checking accuracy and making forecasts.
Assessing Accuracy
You want to know if your model makes good guesses. Microsoft Fabric Notebooks help you check how well your model does. You can use different ways to measure accuracy. Each way tells you something special about your model.
Here is a table with some common ways to check models:
For Sales Forecasting, you often use mean absolute error (MAE) and root mean squared error (RMSE). These help you see how close your guesses are to real sales. Here is a quick table to compare them:
Tip: Use both MAE and RMSE to see how your model does. If they are very different, check your data for outliers.
Many things can change how good your forecasts are. Clean data, looking at old sales, and picking the right way to forecast all matter. Here is a table to help you remember what to check:
Generating Forecasts
Now you can use your model to guess future sales. Microsoft Fabric Notebooks give you many ways to do this:
Try AI samples in Fabric to see how Sales Forecasting works.
Train your model on old sales, then use it to guess sales for new products or groups.
Follow step-by-step guides in Fabric to help you forecast.
When you make forecasts, you get numbers that show what sales might be later. But forecasts are never perfect. You can use special tools to show this, like confidence intervals or prediction ranges. These tools help you see the range of possible results, not just one number.
Note: Showing uncertainty is important. It helps you and your team know that forecasts are not exact. You can use charts with shaded parts or error bars to show this in your reports.
After you make your forecasts, look at the results and ask yourself a few questions: Do the guesses match what you expect from old trends? Are there any big surprises or sudden changes? How wide is the range of possible results?
By checking your results and sharing them with your team, you make sure everyone knows what the forecasts mean. This helps your business plan better and react to changes fast.
Visualization and Sharing
Visualizing Results
You want your sales data to tell a clear story. Microsoft Fabric Notebooks make it easy to show both past and future sales with charts and dashboards. Start by thinking about who will see your visuals. Some people want a quick overview. Others need deep details. You can design different views for each group.
Here’s a simple way to create strong visuals:
Set your goal. Decide what you want your dashboard to show.
Pick the right chart. Use line charts for trends, bar charts for comparisons, and heatmaps for patterns.
Answer questions with each visual. For example, show which product sells best or when sales peak.
Keep your design neat. Use the same style and layout across your dashboard.
Add labels that make sense. Clear names help everyone understand your charts.
Use white space. Spread out your visuals so they are easy to read.
Make your visuals interactive. Add filters so users can explore the data.
You can use Power BI inside your notebook. The Powerbiclient Python package lets you build and view reports right in Fabric. You can turn your data into interactive charts using Spark or pandas DataFrames. This helps you and your team see trends and spot changes fast.
Tip: Give users tools to customize their view. Filters and pop-ups help people find what matters most to them.
Sharing Insights
Sharing your results helps everyone make better choices. Microsoft Fabric Notebooks offer many ways to share your work. You can run and update Python or PySpark workflows right in the platform. If you want to show your forecasts in real time, connect your results to Power BI dashboards. These dashboards let people see updates as soon as new data comes in.
You can also work together with your team. Fabric has built-in tools for sharing notebooks, reports, and datasets. Everyone can see the latest results and add their ideas. Here’s a quick look at sharing methods:
Note: When you share your insights, you help your team plan better and respond quickly to changes.
You can now follow easy steps to make good forecasts in Microsoft Fabric Notebooks. First, get your data ready. Then, look for patterns and trends. After that, build your model. Share your results with your team. Each step helps you make better choices for your business. You can link your Python models to Power BI. This lets you make interactive reports. You can also use MLFlow to keep track of your tests. Try comparing models with tools like Prophet. Make dashboards that change as new data comes in. This way, everyone can see forecasts, even if they do not know how to code.
FAQ
How do I start a new notebook in Microsoft Fabric?
You click the “New Notebook” button in your Fabric workspace. Give your notebook a name. Then, you can start adding code cells and notes right away.
Can I use Python and SQL together in one notebook?
Yes! You can mix Python and SQL in the same notebook. Just pick the right language for each cell. This helps you use the best tool for each step.
What should I do if my data looks wrong after cleaning?
Check your cleaning steps. Go back to your raw data. Try running your cleaning code one step at a time. You can also use charts to spot mistakes fast.
How can I share my sales forecasts with my team?
You can publish your results to Power BI dashboards. Or, share your notebook directly in Fabric. Your team can see updates and add comments in real time.
Do I need to know machine learning to use Fabric Notebooks for forecasting?
No, you do not need to be an expert. Fabric Notebooks have guides and sam