Learn Data Science using Python

Another post starts with you beautiful people! In  previous post  we have learnt keras workflow. In this post we will understand how to solve a image related problem with a simple neural network model using keras. For this exercise we will use  MNIST hand written digit dataset .  The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. This is a very popular dataset to get started with images. You can download this dataset from this link . In this dataset, each digit shows an image and each image is composed of 28 pixel by 28 pixel grid. The image is represented by how dark each pixel is. So zero will be darkest possible while 255 will be lightest possible. Our goal is to create a deep learning model that will predict which digit it is . Here 28 x 28 pixels grid are flattened to 78...

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...