Vernie  Heller

Vernie Heller


Baby Steps Towards Data Science: Decision Tree Regression in Python

What is Decision Tree Regression?

Decision trees are majorly used in classification problems however, let us try to understand its implications in regression and also, try to understand why using it in regression isn’t a great idea.

Decision tree regression enables one to divide the data into multiple splits. These splits typically answer a simple if-else condition. The algorithm decides the optimal number of splits in the data. Since this method of splitting data closely resembles the branches of a tree, this is probably is known as a decision tree. In fact, the last level (i.e., Fit, Unfit) are known as leaves.

For example, look at the image above, there are data regarding customers, their age, whether they eat pizza or not and whether they exercise or not. By performing decision tree regression, the data is split into 2 categories by age i.e., age< 30 and age> 30. Within the age< 30 category, the data is again split into 2 categories by their eating habits i.e., people eating pizza and people not eating pizza. The same goes for exercise as well. By doing these splits, we can simply account for the behavior of the customers based on their choices and we end up deciding whether they are fit or unfit.

Implementation in Python

Let us deep dive into python and build a polynomial regression model and try to predict the salary of an employee of 6.5 level(hypothetical).

Before you move forward, please download the CSV data file from my GitHub Gist.
Once you open the link, you can find "Download Zip" button on the top right corner of the window. Go ahead and download the files.
You can download 1) python file 2)data file (.csv)
Rename the folder accordingly and store it in desired location and you are all set.If you are a beginner I highly recommend you to open your python IDE and follow the steps below because here, I write detailed comments(statements after #.., these do not compile when our run the code) on the working of code. You can use the actual python as your backup file or for your future reference.

Importing Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#python #machine-learning #analytics

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Buddha Community

Baby Steps Towards Data Science: Decision Tree Regression in Python
akshay L

akshay L


Data Science With Python Training | Python Data Science Course | Intellipaat

In this Data Science With Python Training video, you will learn everything about data science and python from basic to advance level. This python data science course video will help you learn various python concepts, AI, and lots of projects, hands-on demo, and lastly top trending data science and python interview questions. This is a must-watch video for everyone who wishes o learn data science and python to make a career in it.

#data science with python #python data science course #python data science #data science with python

Uriah  Dietrich

Uriah Dietrich


How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Data Science with Python Certification Training in Chennai

Learn Best data science with python Course in Chennai by Industry Experts & Rated as and Best data science with python training in Chennai. Call Us Today!

#data science with python training #data science with python courses #data science with python #data science with python course

Manish Sharma


Applied Data Science with Python Certification Training Course -IgmGuru

IgmGuru’s Data Science with Python certification course has been designed after consulting some of the best in the industry and also the faculty who are teaching at some of the best universities. The reason we have done this is because we wanted to embed the topics and techniques which are practiced and are in demand in the industry – conduct them with the help of pedagogy which is followed across universities – kind of applied data science with python. In doing so, we make our learners more industry/job-ready. IgmGuru’s Data Science with Python online training course is the gateway towards your Data Science career.

#applied data science with python #data science with python certification #data science with python online training #data science with python training

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition