Most of the E-commerce companies moved from inventory-led model to market place model in recent times. Mainly because in inventory led model one simply don’t harness the potential of internet as platform. Where as in Market place model you empowers someone who can source an item and you let someone buy who is looking for an item. In market place model there is a buyer on one side and seller on one side and the company in middle simply manage the payment and fulfillment and catalogue along with other aspects.

Pandas is a package which is built on top of Numpy and provides efficient implementation of DataFrame. These dataframes are multidimensional arrays with attached row and column labels and often with heterogenous types.In this post we will focus on Series, DataFrame and related structure effectively. Installing and Using Pandas Once pandas is installed you can import and check the version. import pandas as pd pd.__version__ ## '0.24.2' Pandas Objects There are three fundamental pandas objects or structures : the Series, DataFrame and Index.

Numpy Numpy (Short for Numerical Python) provides an efficient interface to Store and operate on arrays of number. Its somewhat looks like python’s builtin list type but much more efficient as the arrays grows in size. import numpy as np np.__version__ ## '1.8.0rc1' How numpy improves the performances as compare to base python operartions on arrays of data? Python is dynamic typing language unlike C and JAVA which are statically type language that requires variable to be declare explicitly.

Python was released with philosophy which emphasizes simplicity, code readibility and efficiency.Being an Object Oriented Programming language python groups data and code into objects that can interact with and modify one another. Data Science is small portion with in diverse python ecosystem. Python has deep learning and machine learning libraries which includes scikit-learn, Keras and tensorflow. Help and Documentation in python: Every python object contains the reference to a string, known as doc string, which contain a concise summary of the object and how to use it.

Python is an “Object Oriented Programming language” or OOP for short. It is a programming paradigm which focuses on reusable pattern of code, in contrast to procedural programming, which focuses on explicit sequenced instructions. When working on complex programs, object oriented programming let you reuse code and write code that is more readable, which in turn makes it more maintainable. Table of contents Classes and objects Attributes and methods in class Instance attribute Class attribute Object inheritance Parent Vs Child class 1.

Python is most popular programming language among data scientists across the world and like high level languages, python is easy to read, take less time to write and is portable. So in this post we will learn about basics of python which includes variables, numbers conditionals, functions, list, dictionary, set, expression etc… Table of Contents:- Variable, Numbers and Strings Conditionals Functions List, dictionary, Set, Expression 1.

In this post we will use a very elegant and simple approach to test any hypothesis. This approach is based on growing trend of emphasizing data and simulations instead of classical probability theory and complex statistical tests. Since We know that its hard to wrap the head around how to reject null hypotheses and interpret p-values. The new approach however has this philosphy that there is only one statistical test and that at their core, all statistical tests (be they t-tests, chi-squared tests, signed Wilcoxon rank tests, etc.

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks. Below video discusses about gradient descent algorithm. Its good to spend some time on this video and get intuition about it before we start writing code for it.

In this post we will see demo of two machine learning problems using python. We will train model on two different datasets using two different type of machine learning algorithm one for classifier and other for regression.The learning algorithm finds the pattern in the training data which maps the input data features to target variable and it outputs an machine learning model that captures these patterns. Also we will learn about scikit-learn library in detail (coming soon).

Softmax is a generalization of logistic regression which can be use for multi-class classification. The softmax function squashes the outputs of each unit to be between 0 and 1, just like a sigmoid function. But it also divides each output such that the total sum of the outputs is equal to 1. Softmax Function :- Softmax is a generalization of logistic regression which can be use for multi-class classification.