Every Organization wants to succeed and be as profitable as it can. However, there are some obstacles to achieving such success. These are some of the symptoms of obstacles:

There are ways in which you can improve your customer experience:

A feedback mechanism…


What’s the concept?

Exploratory data analysis is a set of techniques that have been principally developed by John Tukey, John Wilder since 1970. The philosophy behind this approach is to examine the data before applying a specific probability model. According to John Tukey and John Wilder, exploratory data analysis is similar to detective work.

Exploratory data analysis (EDA ) was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments.

“Greatest value of a picture is when it forces us to notice what we never expected…


Abstract

In this paper, we will study an integrated approach for studying food industries around the world. We will get an overview of various sectors of an Economy and how food industry is inter-connected with various sectors. We will identify the issues faced by food industry. We will analyse how science and technology is helping food industry. The emergence technologies such as Big Data ad Artificial Intelligence and its current applications in this industry. Finally, we will identify the areas of improvement with the focus of artificial intelligence.

Introduction

Food Industry is the most important industry in world today. Now…


Retail businesses sell items or services to customers for their consumption, use, or pleasure. They typically sell items and services in-store but some items may be sold online or over the phone and then shipped to the customer. Examples of retail businesses include clothing, drug, grocery, and convenience stores.

Different types of Retailers and their offerings:


Principal component analysis (PCA) is a dimension reduction process that allows reducing number of variables from a given dataset to a smaller set of variables that can be used in data analysis. PCA can be defined formally as a statistical procedure used to map a set of interrelated variables into a smaller set of linearly uncorrelated variables while retaining as much variance as possible in the original dataset.


With applications like speech recognition, image processing, language translation Deep learning has seen significant success in recent time. Deep neural networks in general refer to neural networks with many layers and large number of neurons, often layered in a way that is generally not domain specific.

Availability of compute power and large amount of data has made these large structures very effective in learning hidden features along with data patterns. Convolutional neural networks (CNNs) found their success in image recognition problems.


Natural language processing (NLP) is understanding and interpreting human languages, spoken or written, using machine processing or Machine learning.

NLP is useful in a variety of applications including speech recognition, language translations, summarization, question responses, speech generation, and search applications.

NLP is an area of research which has proven to be difficult to master. Deep learning techniques have started to solve some of the issues involved in natural language processing which was earlier solved through Machine Learning techniques.


A Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Neural networks are networks of interconnected artificial neurons thus called as Artificial neural networks (ANNs). Their structure is heavily inspired by the brain’s neuron network.

A neural network is generally used to create supervised machine learning models for classification, similar to a Logistic Regression model, and is useful in cases where Logistic Regression may not provide reasonable accuracy. Neural networks form the basis of many of the complex applications and algorithms of machine learning.

Neural networks are also used in unsupervised learning for compressed…


Random Forests models are supervised machine learning algorithm used in classification and regression problems. A Random Forest is a collection of Decision Trees that improve the prediction over a single Decision Tree.

Decision Tree

A tree is an acyclic directed data structure with nodes and edges that connect nodes.

A Decision Tree is a tree with nodes representing deterministic decisions based on variables and edges representing path to next node or a leaf node based on the decision. Leaf node or terminal node of the tree represents a class label as output of prediction.

A Decision Tree is built by…


Clustering works with the assumption that elements which are closer to each other would have similarity in properties. Various types of clustering algorithms are given below:

1. K-Means

It is an unsupervised learning algorithm. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

K-means algorithm is about segregating input data into K clusters for a predefined K.

Each of the data point in the input set is an unlabelled data. The interpretation for each of K clusters can be that the mean value for a cluster is…

Aniket Patil

Product Management, Product Marketing, Data Science, AI, Book Reivew

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