Saurabh Parab

Before Implementing a Linear Regression model,its important to go through the assumptions.As linear Regression is a parametric algorithm, It's important that the data satisfies the following assumptions So lets go through it one by one. 1.Linearity This assumes that there is a linear relationship between the predictors (e.g independent variable) and the response variable(e.g dependent variable). You  can use the scatter plot to detect the linearity of the variables. 2.No Outliers There should be no outliers in the data.You can check for outliers with the help of box plot 3. No Multicollinearity There should be no multicollinearity between the independent variables. 4. Autocorrelation There should be no correlation between the residual (error) terms.Absence of this concept is known as autocorelation. 5. Normality The dependant varible should be normally distributed.If not you convert it through log function. It’s not uncommon for assumptions to be violated on real-world data, but it

Welcome back on our  journey to learn the basics of Machine learning.Before having a hands on on real time data I thought making you all familiar with various types of machine learning algorithms.Let as now breakdown the classification of Machine Learning.  Let us go through each type one by one. 1. Supervised Learning :  Imagine you are a student in a class.Teacher trains you by giving you  knowledge about a subject. Supervised learning is like learning from a teacher  here the machine is you(student) and the teacher is the training data(the data   we  discussed in the previous blog, one stored in the excel file).Training data   will be used to train the   model. Supervised Learning is further divided into two types:-   Regression Classification Regression includes a scenario where machine is trained to predict some value like price ,height and weight other example will be  predict stock market prices etc.Algorithms which can perform regression are Linear Regression,Decision Tree, S

Here we are going to discuss about the base level concept of the machine learning. There are numerous algorithms in Machine Learning But, Linear regression is the oldest ,simplest and widely used supervised learning algorithm for predictive analysis. What is Linear Regression? It’s a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable. Application in Real Life:- Say you are planning a Trip to Goa with 2 best buddies.You start off in Mumbai and you know its 12 hr drive. While your friends  are in charge of the party operations you're in charge of the logistics involved. You've to plan :the schedule,when to stop and where ,make sure you reach on time ,etc So,Whats the first thing you do?? Bring a sheet of paper and start your plan!!  First item in your checklist is Budget .  Its a 12 hr drive -approximately 600 km. the fun ride ,so it should take a total of 15 hrs on the road. The next question will be "