Nearly every data-driven company utilizes the sentiment analysis model to assess its consumers’ behaviour towards their business products. This project will be great for you if you’re fascinated with machine learning and want to increase your expertise in it. This R project is focused on classification. Sentiment analysis referred to the process of evaluating and categorizing views expressed in a piece of feedback, particularly for determining whether the customer’s behaviour is positive, negative, or neutral towards a particular product.
That’s where sentiment analysis can help to:
- Understand what your customers like and dislike about your product.
- Compare your product reviews with those of your competitors.
- Get the latest product insights in real-time, 24/7.
- Save hundreds of hours of manual data processing.
- Sentiment analysis is the automated process of
- understanding the sentiment or opinion of a given text.
- You can use it to automatically analyze product reviews and sort them by Positive, Neutral, Negative.
The best part. You can start analyzing your product reviews for sentiment right away.
Fake News Detection Using R Language
Fake news is pervasive and spreads 10 times more quickly than actual news. This is a major cause of trouble that has affected every aspect of the life of the average person. Numerous issues arise as a result, including political division, violence, and other cultural disputes. Considering the best way to track and handle this issue! This project for detecting false news uses data from the R language and accurately labels the two types of information while also representing the textual data in the proper way. Later, for a superior estimate of what is true or fake, we may include the concepts of NLP, or Natural Language Processing, and TF-IDF Vectorizer approach (whose full name is term frequency-inverse document frequency vectorizer). Therefore, one need not worry about whether social authenticity
Creating your First Chatbot In Python
By monitoring and effectively resolving all client complaints that arise in real time, chatbots allow businesses to become more customer-centric. Considering how to accomplish this now! These chatbots have certain conversational NLP scripts running that allow them to comprehend the questions and then respond with the answers in the form of customer-focused feedback. For the purpose of this project, the Python language accesses a bigger volume of data via an Intents JSON file. These patterns will be useful in returning the right answers the user wants to get in order to solve his or her problem. Such answers might, if necessary, be synchronised with the necessary adjustments to effectively handle open-domain or domain-specific issues.
Detecting Frauds of Credit Cards
In the current epidemic period, fraudsters are mostly responsible for credit card theft. Such individuals are cunning enough to steal your credit card information, including the CVV and Card Numbers, and use it to gain unauthorised access to your account. The likelihood of catching such fraudulent fraudsters essentially disappears given the multitude of digital methods available to access someone’s account. Considering ways to maximise the likelihood of detecting these fraudsters! Insights into the customer’s data will be labelled with proper modelling of their spending pattern thanks to this CC Fraud Detection project, which combines hidden capabilities of machine learning, artificial neural networks, and decision trees.
These con artists will undoubtedly keep tabs on individuals who spend more money so they can effectively take their users’ financial independence. With such tracking, the likelihood of stopping such fraudsters from acting in accordance with their true desires increases, preventing the privacy of information and improving overall accuracy.
Using Deep Learning for the Classification of Breast Cancer
Due to the infrequent implementation of awareness campaigns, breast cancer is the second most prevalent disease detected globally. You could believe that one might successfully combat breast cancer in our highly developed modern world full of options. To some extent, this is true, however if there is a wait, such remedies won’t work their wonders. Therefore, it is crucial to determine the characteristics of this type of cancer, and you may help with this by choosing Breast Cancer Classification as your assignment. Since invasive ductal carcinoma in the breast is the most common kind of breast cancer and affects more than 70% of patients, IDC, or the Invasive Ductal Carcinoma dataset, would be used in this instance.The advantage is that this dataset will combine all of the diagnostic images of cells that cause cancer, and with the aid of Deep Learning attributes, the classification of patients—whether they have this type of cancer or not—will be done precisely, making it simpler to understand the complexity of a patient’s situation. If necessary, the analysis will be used later to the patient’s advantage so that they can recover as quickly as possible from the effects of breast cancer.
Sales forecast using data science
Big data and data science are used in e-commerce and retail to streamline corporate procedures and support successful decision-making. Data science approaches are used to effectively handle a variety of operations, including forecasting sales, providing product suggestions to clients, and managing inventories. Walmart generated $482.13 billion in sales in 2016 thanks to accurate estimates made possible by data science approaches among its 11,500 employees. As implied by the project’s title, you will work with the Walmart store dataset, which comprises of 143 weeks’ worth of sales information from 45 Walmart shops and their 99 divisions.
Building a Resume Parser Using NLP(Spacy) and Machine
The period when recruiters spent a considerable amount of time manually screening resumes is long past. Thanks to resume parsers, sorting through thousands of applicants’ applications for a job is no longer a difficult chore. In order to intelligently scan through hundreds of resumes and choose the best applicant for a job interview, resume parsers employ machine learning technologies.
A resume parser: what is it?
A software known as a resume parser or CV parser analyses and extracts CV/ Resume data in accordance with the job description and produces output that is machine-readable and appropriate for computer storage, modification, and reporting. Using a resume parser, recruiters may compile a list of resumes by storing the information that has been retrieved for each one with a distinct entry.
Movie Recommendation Platform with R Packages
Similar to Netflix, YouTube, and Hotstar, the movie recommendation platform will operate. The suggestions will be predicted using R packages while taking into account the customers’ tastes, star cast, genre, and browsing history. Still unsure about the benefits of this method. By informing the options approved by the variety of users, the system may be able to address all movie search shortcomings. In addition, the project may be developed using either one of two techniques: collaborative filtering or content-based filtering. When deciding what to watch or not, the collaborative will take into account a user’s prior movie-watching habits.
Contrarily, content-based filtering makes use of a number of distinct traits that are entirely determined by the summary and profile of a movie that was recently or previously seen. In all of these, it is possible to model the required movie suggestions exactly and amusingly using R tools like data.table, ggplot2, and recommenderlab. As a result, you must choose this platform as your project and thoroughly train it to categorise and propose movies with various themes and interests.
Loan Default Prediction
Loans are the main source of income for banks since a sizable amount of their profits come straight from the interest on these loans. However, approving a loan requires a drawn-out validation and verification process that depends on a number of factors. Banks are also dubious as to whether a borrower would be able to repay the loan without incident despite thorough verification. Today, almost all banks use machine learning to automate the loan qualification process in real-time based on a variety of factors, such as credit score, marital status, job status, gender, existing loans, the total number of dependents, income, and spending, among others.