How to Create simulated data for regression in Python?

How to Create simulated data for regression in Python?

How to Create simulated data for regression in Python?

This recipe helps you Create simulated data for regression in Python


Recipe Objective

Many times we need dataset for practice or to test some model so we can create a simulated dataset for any model from python itself.

So this is the recipe on we can Create simulated data for regression in Python.

Step 1 - Import the library

import pandas as pd from sklearn import datasets

We have imported datasets and pandas. These two modules will be required.

Step 2 - Creating the Simulated Data

We can create Datasets for regression by passing the parameters which are required for regression like n_samples, n_features, n_targets etc. The function will give the output as a dataset features, output and coefficient. features, output, coef = datasets.make_regression(n_samples = 80, n_features = 4, n_informative = 4, n_targets = 1, noise = 0.0, coef = True)

Step 3 - Printing the Dataset

Here we have printed the dataset's different components i.e. Features, Output and Coef. print(pd.DataFrame(features, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4']).head()) print(pd.DataFrame(output, columns=['Target']).head()) print(pd.DataFrame(coef, columns=['True Coefficient Values'])) So the output comes as

   Feature_1  Feature_2  Feature_3  Feature_4
0  -0.061616   0.322765   1.329021  -0.975053
1   0.489019  -0.838662   0.445058  -0.244990
2   0.324046   0.656792  -0.034017  -1.445877
3   0.227775  -0.174360   0.652398  -0.336352
4   0.837811  -2.410269  -0.368019  -1.066476

0  -68.619492
1  -16.114323
2 -122.108491
3  -18.132927
4 -124.770731

   True Coefficient Values
0                26.722153
1                15.494463
2                17.067228
3                97.078600

Relevant Projects

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.