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

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

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.

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.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

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.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.