Learn Data Science using Python

Another post starts with you beautiful people! Hope you have learnt something from my previous post about  machine learning classification real world problem Today we will continue our machine learning hands on journey and we will work on an interesting Credit Card Fraud Detection problem. The goal of this exercise is to anonymize credit card transactions labeled as fraudulent or genuine. For your own practice you can download the dataset from here-  Download the dataset! About the dataset:  The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. Let's start our analysis with loading the dataset first:- As per the  official documentation -  features V1, V2, ... V28 are the principal compo...

Another post starts with you beautiful people! Hope you have learnt something new and very powerful machine learning model from my previous post-  How to use LightGBM? Till now you must have an idea that there is no any area left that a machine learning model cannot be applied; yes it's everywhere! Continuing our journey today we will learn how to deal a problem which consists texts/sentences as feature. Examples of such kind of problems you see in internet sites, emails, posts , social media etc. Data Scientists sitting in industry giants like Quora, Twitter, Facebook, Google are working very smartly to build machine learning models to classify texts/sentences/words. Today we are going to do the same and believe me friends once you do some hand on, you will be also in the same hat. Challenge Link :  jigsaw-toxic-comment-classification-challenge Problem : We’re challenged to build a multi-headed model that’s capable of detecting different types of toxicity like thre...

Another post starts with you beautiful people! First of all thank you everyone for visiting my blog and showing your keen interest in Linear  and Logistic  Regression topics of Machine Learning track! Since many of you have asked a common but most important question- How to know when and where apply either Linear or Logistic regression? Therefore I am going to share this post where I will try to resolve your doubt. Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling. Each form has its own importance and a specific condition where they are best suited to apply- What is Regression Analysis? Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor).  This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables.  For example, relatio...

Another post starts with you beautiful people! I appreciate that you have shown your interest in Machine Learning track and enjoyed my previous post about  Linear Regression   where we learned the concept with the case study of bike sharing system. Today we will continue our Data Science journey and learn about  Logistic Regression . Like all regression analyses, the logistic regression is a predictive analysis. The fact is that linear regression works on a continuum of numeric estimates. In order to classify correctly, we need a more suitable measure, such as the probability of class ownership . Thanks to the following formula, we can transform a linear regression numeric estimate into a probability that is more apt to describe how a class fits an observation : probability of a class = exp(r) / (1+exp(r)) r is the regression result (the sum of the variables weighted by the coefficients)  exp is the exponential function.  exp(r) corresponds to Eu...