Kolby Wyman

Kolby Wyman

1593326280

Machine Learning Basics: Simple Linear Regression

One would perhaps come across the term “Regression” during their initial days of Data Science programming. In this story, I would like explain the program code for the very basic “Simple Linear Regression” with a common example.

Overview —

In statistics, **_Linear Regression _**is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). In our example, we will go through the Simple Linear Regression.

Simple Linear Regression is of the form y = wx + b, where y is the dependent variable, x is the independent variable, w and b are the training parameters which are to be optimized during training process to get accurate predictions.

Let us now apply Machine Learning to train a dataset to predict the Salary from Years of Experience.

Step 1: Importing the Libraries

In this first step, we shall import the **_pandas _**library that will be used to store the data in a Pandas DataFrame. The **_matplotlib _**is used to plot graphs.

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

Step 2: Importing the dataset

In this step, we shall download the dataset from my github repositary which contains the data as “data.csv”. The variable _X _will store the “Years of Experience” and the variable _Y _will store the “Salary”**_. _**The dataset.head(5)is used to visualize the first 5 rows of the data.

dataset = pd.read_csv('https://raw.githubusercontent.com/mk-gurucharan/Linear-Regression/master/data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

dataset.head(5)
>>
YearsExperience Salary
1.1             39343.0
1.3             46205.0
1.5             37731.0
2.0             43525.0
2.2             39891.0

#regression #machine-learning

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Machine Learning Basics: Simple Linear Regression
sophia tondon

sophia tondon

1620898103

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Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Michael Hamill

Michael Hamill

1618296540

Linear Regression- Machine Learning

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.
Linear regression algorithms show a linear relationship between a dependent (y) and one or more independent (y) variables, hence called linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable.
When working with linear regression, our main goal is to find the best fit line that means the error between predicted values and actual values should be minimized. The best fit line will have the least error.
The different values for weights or the coefficient of lines (a0, a1) gives a different line of regression, so we need to calculate the best values for a0 and a1 to find the best fit line, so to calculate this we use cost function.

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