When we come across a business analytical problem, without acknowledging the stumbling blocks, we proceed towards the execution. Before realizing the misfortunes, we try to implement and predict the outcomes.
But do those outcomes reveal the strategies to decode the problems?
The answer is NO. We can’t come up with a solution having zero significance in business understanding. To improve the quality of the product, create market strategies, establish brand perceptions, and upgrade customer satisfaction, we have to demolish the complications.
At the end of this article, you will get to know about the problem-solving steps involved in the data…
Before reading the article, I would like it if you take some time and think about the changes in your social and professional behavior since you have started using social media.
Have you got anything?
Don’t worry. Take a long breath and start reading the article.
“THE TECHNOLOGY THAT CONNECTS US, ALSO CONTROLS US.” — Social Dilemma
Social media isn’t a tool that’s just waiting to be used. It has its own goals and it has its own means of pursuing them. There are 3.81 billion social media users in the world with an increase of 9.2% users every year…
Image Classification is the technique to extract the features from the images to categorize them in the defined classes. It is a common-sense problem for the human to identify the images but, for the machine, it’s definitely not. At first, we have to train the machine with the images, and later, when we introduce the model with a new image then based on the training it will give us significant results.
In this article, we will understand how convolutional neural networks are helpful and how they can help us to improve our model’s performance. …
Let’s come directly on the point, in this article, we will try to develop a machine model on “Default of Credit Card Clients Dataset” hosted on Kaggle and precut whether a customer will default the payment next month.
This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.
There are 25 variables:
Emotion is the state of mind that is aligned with feelings, and thoughts usually directed toward a specific object. Emotion is a behavior that reflects personal significance or opinion regarding the interaction we have with other human beings or related to a certain event. The human being is able to identify the emotions from textual data and can understand the matter of the text. But if you think about the machines, can they able to identify the emotions from the text?
From this article, you will understand how to use this python package and extract the emotions from the text…
When we handle text data, we always have concerns about the data features, pre-processing of data, and more likely the predictions. To improve our model, it is important to understand the data and find the more interesting features in the data like hashtags, links, and many more.
This is a python package that helps you to extract the basic features from the text data such as hashtags, stopwords, numerics which will help you to understand the data and improve your model more effectively.
Function call structure:
function_name(dataframe, ”text_column”, ”new_column”)
dataframe:- name of dataframe
text_column:- name of the column from…
K-Nearest Neighbors is the supervised machine learning algorithm used for classification and regression. It manipulates the training data and classifies the new test data based on distance metrics. It finds the k-nearest neighbors to the test data, and then classification is performed by the majority of class labels.
Selecting the optimal K value to achieve the maximum accuracy of the model is always challenging for a data scientist. I hope you all know the basic idea behind the KNN, yet I will clarify an overview of knn later in this article. …
Supervised machine learning categorizes into regression and classification. We use the regression technique to predict the target values of continuous variables, like predicting the salary of an employee. In contrast, we use the classification technique for predicting the class labels for given input data.
In classification, we design the classifier model, then train it using input train data and then categorize the test data into multiple class labels present in the dataset.
Let us understand the concept in-depth,
Whenever a user wants to share his opinion regarding any trending topic on social media, we try to find the sentiment score of that expressed opinion using sentiment analysis. Twitter is the most widely used micro-blogging social media platform, having nearly 145 million daily active users. Nowadays, the user posts the tweets using hashtags, emoticons, abbreviations, and puns, which gets challenging to analyze the tweet and formulate sentiment scores.
In this article, I tried to perform Vader sentiment analysis along with tweepy on twitter data, which is a Python-based approach. This twitter sentiment analysis is basically for the market research…
A while ago whenever we bought a specific product, it was probably recommended by our friends or trusted persons. But now the scenario has changed, what the product recommendations on amazon or movie recommendations on Netflix we are getting that are basically on our own interests. By analyzing the customer’s current site usage and his previous browsing history, a recommendation engine studies customer behaviour and his interests. Based on this information it is able to deliver relevant product recommendations. …