MindsDB is an Open-Source AI Layer for databases, For more details, Checkout this blog to know more
This tutorial will teach you how to train a model to predict mobile worth based on many variables, such as clock_speed, battery_power, dual_sim, etc.. with 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.mobileprice_predictor FROM files (SELECT * FROM MobilePrices LIMIT 10000) PREDICT price_range;
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='mobileprice_predictor'
Once it returns
complete we can start predicting with it!
Describe the Model
Before we proceed to the final part of predicting mobile phone prices, 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
This query shows the role of each column for the predictor model along with the type of encoders used on the columns while training.
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
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
We will start by predicting that only 1 feature parameter is supported by price_range and therefore the query should look like this.
NOTE: While predicting always input multiple feature parameters as the prediction accuracy degrades.
SELECT price_range FROM mindsdb.mobileprice_predictor WHERE dual_sim ='0';
The predicted price range should be 0
SELECT price_range FROM mindsdb.mobileprice_predictor WHERE touch_screen ='0' and int_memory = 51;
The price range should be 3
Mobilistic! We have now successfully predicted the mobile phone price range with MindsDB
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.
Latest comments (0)