Introduction
MindsDB is an Open-Source AI Layer for databases, For more details, Checkout this blog to know more
In this tutorial we will be predicting the quality of wine based on its features using MindsDB.
Importing Data into MindsDB Cloud
In order to import the dataset to MindsDB Cloud, we need to first download it from Kaggle and then upload it simply to MindsDB using the steps mentioned below.
Step 1: Create a MindsDB Cloud Account, If you already haven't done so
Step 2: Download this Dataset
Step 3: Go into Add Data -> Files -> Import File
Lastly Add the dataset (After downloading you will get a .zip file, You have to extract it and import the csv inside)
Step 4: Name the Table
Step 5: To verify the dataset has successfully imported in:
Run this Query:
SHOW TABLES FROM files;
If you see the dataset, then it's imported successfully!
We have imported our dataset into MindsDB, next up we will be creating a Predictor Model!
Training a Model
Step 1: Creating a Predictor Model
MindsDB provides a syntax which exactly does that!
CREATE PREDICTOR mindsdb.predictor_name (Your Predictor Name)
FROM database_name (Your Database Name)
(SELECT columns FROM table_name LIMIT 10000) (Your Table Name)
PREDICT target_parameter; (Your Target Parameter)
Simply replace the paramaters with the ones you want to use
CREATE PREDICTOR mindsdb.wine_predictor
FROM files
(SELECT * FROM WineFeatures LIMIT 10000)
PREDICT quality;
Step 2: Based on the size of the dataset, it might take some time.
There's 3 stages once you run the command to create the model:
- Generating: The model's generating!
- Training: Model is getting trained with the dataset
- Complete: The model is ready to do predictions
To check the status, this is the query:
SELECT status
FROM mindsdb.predictors
WHERE name='wine_predictor'
Once it returns complete we can start predicting with it!
Describe the Model
Before we proceed to the final part of predicting the wine quality, let us first understand the model that we just trained.
MindsDB provides the following 3 types of descriptions for the model using the DESCRIBE statement.
- By Features
- By Model
- By Model Ensemble
By Features
DESCRIBE mindsdb.predictor_model_name.features;
This query shows the role of each column for the predictor model along with the type of encoders used on the columns while training.
By Model
DESCRIBE mindsdb.predictor_model_name.model;
This query shows the list of all the underlying candidate models that were used during training. The one with the best performance (whose value is 1), is selected. You can see the value 1 for the selected one in the selected column while others are set at 0.
By Model Ensemble
DESCRIBE mindsdb.predictor_model_name.ensemble;
This query gives back a JSON output that contains the different parameters that helped to choose the best candidate model for the Predictor Model.
As we are done understanding our Predictor model, let's move on to predicting values.
Predicting the Target Value
It should be noted that the quality of wine is determined by multiple feature values altogether. The accuracy may degrade if some of these feature values are left out.
But let us still try to predict the wine quality based on a few feature sets with a query like this.
SELECT quality,quality_confidence,quality_explain
FROM mindsdb.wine_predictor
WHERE pH=3.3 AND density=0.997 AND alcohol=9.4 AND sulphates=0.46;
As the predicted quality is 5, It states that the wine is neither good nor bad.
It's time to pass values of all the features to have a more accurate prediction.
SELECT quality,quality_confidence,quality_explain
FROM mindsdb.wine_predictor
WHERE pH=3.5 AND density=0.9976 AND alcohol=13 AND sulphates=0.86
AND fixed_acidity=10.3 AND volatile_acidity=0.3 AND citric_acid=0.72
AND residual_sugar=2.5 AND chlorides=0.075 AND free_sulfur_dioxide=11
AND total_sulfur_dioxide=20;
As the predicted Quality is 8, this wine is good!
Great! We have now successfully predicted the wine quality with MindsDB
Conclusion
This concludes the tutorial here.
Lastly, before you leave, I would love to know your feedback in the Comments section below and it would be really great if you drop a LIKE on this article.
Top comments (2)
This is a great application of Machine Learning! Love it.
Thanks!