How to define datasets in Pycaret.

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by l.rohitharohitha2001@gmail.com dot icon Updated: Nov 21, 2023

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PyCaret is a Python library that simplifies the process of building and comparing. It provides a high-level interface for various machine-learning tasks.

The datasets module provides access to a collection of publicly available datasets. This can be used for machine learning and data analysis tasks. These datasets are conveniently bundled with PyCaret for quick access and experimentation. The datasets module makes it easy to load and work with these datasets.   

 

Tips for using Datasets in Pycaret:   

 

  1. Explore Available Datasets: PyCaret provides a collection of built-in datasets for practices. Familiarize yourself with the available datasets by reviewing the PyCaret documentation.   
  2. Select the Appropriate Dataset: Choose a dataset that is relevant to your learning. Consider the task you want to perform, whether classification or regression.   
  3. Understand the Dataset: Before diving into model building, take the time to understand. Use Python's Data Frame methods or Pandas to examine the data. 
  4. Data Preprocessing: Depending on the dataset, you may need to perform data preprocessing. It includes handling missing values, encoding categorical variables, and scaling features.   
  5. Target Variable Selection: Ensure you specify the target variable when using the setup. PyCaret must know which column you want to predict.   
  6. Automatic Data Type Detection: Let Py Caret's automatic data type detection. Use the convert datatype function only when you have prior.   

 

In summary, using datasets in PyCaret is an excellent way to streamline and speed up the process. It makes it accessible to users at various skill levels and provides a platform. PyCaret datasets offer valuable advantages.   

 

Here is an example of how to define datasets in Pycaret.

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution we are using Pycaret library of Python.

#%% Imports
# Data manipulation
import numpy as np
import pandas as pd

import pprint # Print a nice output
PP = pprint.PrettyPrinter(indent=4)

#%% List columns
def list_true_columns(x):
    result = []
    for i in range(0,len(x)):
        if x[i] == 1:
            result += [i]
    return result

column_amount = 300
row_amount = 1000

#%% Sample dataset
dataset = pd.DataFrame(np.random.binomial(n=1, p=0.5, size = (row_amount, column_amount)))
# Based on the sample, calculate dependent variable 
dataset['dependent'] = dataset.apply(list_true_columns, axis = 1)
PP.pprint(dataset.head)

    0   1   2   3   4   5   6   7   8   9   ... 291 292 293 294 295 296 297 298 299
0   0   1   1   0   1   1   1   0   1   0   ... 1   1   0   0   0   0   0   1   1
1   1   1   0   0   0   1   0   1   1   0   ... 0   1   1   1   0   1   1   0   1
2   0   1   0   0   1   1   0   1   0   0   ... 0   1   0   1   0   0   1   1   0
3   0   1   0   1   0   0   1   1   1   0   ... 0   0   0   0   0   1   1   0   0
4   1   0   1   1   0   0   0   0   1   0   ... 1   1   1   0   0   0   1   0   1
5   0   0   1   1   1   1   0   1   0   0   ... 1   1   0   1   0   1   1   1   0
..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ... ... ... ... ... ... ... ... ... ...
994 1   1   0   1   1   0   1   1   0   1   ... 0   0   0   1   0   0   1   0   0
995 1   0   1   0   0   0   0   1   0   0   ... 1   1   0   0   0   0   1   0   1
996 1   0   1   0   1   0   0   0   0   1   ... 1   1   0   0   0   1   1   0   1
997 0   0   0   1   0   1   1   0   0   0   ... 1   0   1   1   0   0   0   1   0
998 0   0   0   0   0   1   1   1   1   0   ... 1   0   0   0   1   1   1   1   0
999 0   0   1   0   0   0   1   1   1   1   ... 1   0   0   1   1   1   1   1   1

                                            dependent  
0    [1, 2, 4, 5, 6, 8, 11, 15, 17, 18, 19, 20, 21,...  
1    [0, 1, 5, 7, 8, 12, 15, 16, 17, 18, 19, 20, 24...  
2    [1, 4, 5, 7, 11, 12, 15, 16, 18, 26, 27, 28, 2...  
3    [1, 3, 6, 7, 8, 11, 12, 15, 16, 23, 25, 27, 28...  
4    [0, 2, 3, 8, 13, 16, 18, 19, 20, 21, 22, 28, 2...  
5    [2, 3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 21, 24...  
..                                                 ...   
994  [0, 1, 3, 4, 6, 7, 9, 10, 11, 15, 17, 20, 21, ...  
995  [0, 2, 7, 12, 13, 14, 15, 16, 17, 19, 22, 23, ...  
996  [0, 2, 4, 9, 11, 13, 16, 17, 18, 20, 21, 23, 2...  
997  [3, 5, 6, 11, 14, 20, 21, 22, 24, 28, 30, 35, ...  
998  [5, 6, 7, 8, 13, 17, 19, 20, 22, 23, 24, 28, 3...  
999  [2, 6, 7, 8, 9, 14, 17, 18, 19, 20, 21, 22, 23...

Instructions


Follow the steps carefully to get the output easily.


  1. Download and Install the Jupyter Notebook on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Create a new Python file on your Notebook.
  4. Copy the snippet using the 'copy' button and paste it into your Python.
  5. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for 'How to create a Supervised dataset?' in Kandi. You can try any such use case!

Environment Tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. Jupyter Notebook (anaconda 3) 6.0.1 Version
  2. The solution is created in Python 3.8 Version
  3. Pycaret 2.3.10 Version.


Using this solution, we can be able to use define datasets in Pycaret using Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use define datasets in Pycaret using Python.

Dependent Library


datasetsby huggingface

Python doticonstar image 16438 doticonVersion:2.12.0doticon
License: Permissive (Apache-2.0)

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Support
    Quality
      Security
        License
          Reuse

            datasetsby huggingface

            Python doticon star image 16438 doticonVersion:2.12.0doticon License: Permissive (Apache-2.0)

            🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on kandi like 'datasets'.

                      FAQ:   

                      1. What is PyCaret, and how does it help define datasets for machine learning?   

                      PyCaret is a Python library designed to simplify building and comparing. It provides a high-level interface for various machine-learning tasks. You can use PyCaret to define datasets by loading them and setting data types. It initializes the dataset for analysis and model building.   

                       

                      2. What is the purpose of the setup function in PyCaret when defining datasets?    

                      The setup function in PyCaret is used to configure the dataset for analysis and modeling. It allows you to specify the target variable and set a random seed for reproducibility.   

                       

                      3Can PyCaret automatically detect the data types of columns in my dataset?   

                      Yes, PyCaret can automatically detect the data types of columns. It uses heuristic rules to assign data types to each column. It can also update data types manually using the convert datatype function if needed.   

                       

                      4. What are some common tasks I can perform after defining datasets in PyCaret?   

                      After defining datasets in PyCaret, you can perform tasks such as exploring the data. It compares machine learning models, creating and tuning models, and evaluating model performance. In addition, it deploys the best model for production use.   

                       

                      5. What types of machine learning tasks can I perform using PyCaret datasets?    

                      PyCaret datasets help with various machine learning tasks. This includes classification, regression, clustering, anomaly detection, and natural language processing (NLP). It covers a range of use cases. 

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