Diagnosing residual plots in linear regression models

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Assumptions of Linear Regression Model :There are number of assumptions of a linear regression model. In modeling, we normally check for five of the assumptions. These are as follows :1. Relationship between the outcomes and the predictors is linear. 2. Error term has mean almost equal to zero for each value of outcome. 3. Error term has constant variance. 4. Errors are uncorrelated. 5. Errors are normally distributed or we have an adequate sample size to rely on large sample theory.The point to

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7 Types of Regression Techniques you should know!

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IntroductionLinear and Logistic regressions are usually the first algorithms people learn in predictive modeling. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important amongst all forms of regression analysis.The truth is that there are innumerable forms of regressions, which can be performed. Each form has its own importance and a specific condition where they are

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How to detect Outliers in your dataset and treat them in Machine Learning Algorithms?

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In the last two articles of this series (data exploration & preparation), we looked at Variable identification,Univariate, Bi-variate analysis and Missing values treatment. In this article, we will look at the next step of data preparation: Outlier detection and treatment.What is an Outlier?Outlier is a commonly used terminology by analysts and data scientists as it needs close attention else it can result in wildly wrong estimations. Simply speaking, Outlier is an observation that appears far

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How to avoid Over fitting using Regularization In Machine Learning Algorithms?

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“Among competing hypotheses, the one with the fewest assumptions should be selected. Other, more complicated solutions may ultimately prove correct, but—in the absence of certainty—the fewer assumptions that are made, the better.”Business Situation:In the world of analytics, where we try to fit a curve to every pattern, Over-fitting is one of the biggest concerns. However, in general models are equipped enough to avoid over-fitting, but in general there is a manual intervention required to make

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Feature Engineering

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How to transform variables and create new ones?One of common advice machine learning experts have for beginners is – focus on Feature Engineering. Be it a beginner building his first model or some one who has won Kaggle competitions – following this advice works wonders for every one!I have personally seen predictive power of several models improve significantly with application of feature engineering.What is Feature Engineering?Feature engineering is the science (and art) of extracting more

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