basic

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MindsDB Basics

Here you will find a file home_rentals.csv, containing the final rental pricing for some properties.

Goal

The goal is to be able to predict the best rental_price for a new properties given the information that we have in home_rentals.csv.

Learning

from mindsdb import *

# First we initiate MindsDB
mdb = MindsDB()

# We tell mindsDB what we want to learn and from what data
mdb.learn(
    from_data="home_rentals.csv", # the path to the file where we can learn from
    predict='rented_price', # the column we want to learn to predict given all the data in the file
    model_name='home_rentals' # the name of this model
)

Predicting

from mindsdb import *

# First we initiate MindsDB
mdb = MindsDB()

# use the model to make predictions
result = mdb.predict(predict='rented_price', when={'number_of_rooms': 2,'number_of_bathrooms':1, 'sqft': 1190}, model_name='home_rentals')

# you can now print the results
print('The predicted price is ${price} with {conf} confidence'.format(price=result.predicted_values[0]['rented_price'], conf=result.predicted_values[0]['prediction_confidence']))

Notes

About the Learning

The first thing we can do is to learn from the csv file. Learn in the scope of MindsDB is to let it figure out a neural network that can best learn from this data as well as train and test such model given the data that we have (learn more in InsideMindsDB).

When you run this script, note that it will start printing something like:

Test Error:0.002251171274110675, Accuracy:0.8817534675498809
[SAVING MODEL] Lowest ERROR so far! - Test Error: 0.002251171274110675 

The error is RMSE and the Accuracy is the R squared value of the prediction.

About getting predictions from the model

Please note the when argument, in this case assuming we only know that:

• 'number_of_rooms': 2,
• 'number_of_bathrooms':1
• 'sqft': 1190

So long the columns that you pass in the when statement exist in the data it learnt from it will work (see columns in home_rentals.csv)