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! I hope you have enjoyed and must learnt something from previous two posts about real world machine learning problems in Kaggle. As I said earlier Kaggle is a great platform to apply your machine learning skills and enhance your knowledge; today I will share again my learning from there with all of you! In this post we will work upon an online machine learning competition where we need to predict the the price of products for Japan’s biggest community-powered shopping app. The main attraction of this challenge is that this is a Kernels-only competition; it means the datasets are given for downloading only in stage 1.In next final stage it will be available only in Kernels. What kind of problem is this? Since our goal is to predict the price (which is a number), it will be a regression problem. Data: You can see the datasets  here Exploring the datasets: The datasets provided are in the zip format of 'tsv'. So how can ...