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10 Machine Learning Project Ideas for Beginners

  • Ayush Singh Rawat
  • Sep 19, 2021
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The area of machine learning is always changing. With evolution comes an increase in demand and significance. ‘High-value forecasts that can drive better judgments and smart actions in real-time without human interaction,' is one key reason why data scientists need machine learning.


Here is a list of 10 sample projects that can help you understand the technicalities of machine learning.


Also read: (machine learning tutorial for beginners)


Different Machine Learning Projects


  1. Amazon Product Reviews Sentiment Analysis


Amazon is a global business based in the United States that specialises in e-commerce, cloud computing, digital streaming, and artificial intelligence. However, it is most recognised for its e-commerce platform, which is currently one of the most popular online shopping platforms. 


Amazon makes an average of $ 638.1 million per day due to the large number of customers that purchase items from the company. With such a vast consumer base, analysing the feelings of Amazon product reviews will prove to be a fantastic data science endeavour.



  1. Hate speech detection


On a daily basis, we encounter hate speech on social media platforms such as Twitter and Facebook. The majority of hate speech-related posts may be discovered on the accounts of people who have political beliefs.


Because people's ideas are difficult to categorise as hateful or insulting, there is no legal definition of hate speech. Nonetheless, the United Nations defines hate speech as any type of verbal, written, or behavioural communication that can attack or use discriminatory language regarding a person or a group of people based on their identity, whether that identity is based on religion, ethnicity, nationality, race, colour, ancestry, gender, or any other factor.


I hope you now have a better understanding of hate speech. Hate speech must be detected on social media platforms and either prevented from getting viral or banned at the appropriate moment.


(Similar reading: AI project ideas)



  1. Covid-19 vaccine analysis


Covid-19 had gotten out of hand at one point. Even after the lockdown, there was still a significant surge of cases, since some nations were able to manage instances by sacrificing their economies. Sole vaccinations are considered as the only instrument that can assist the globe combat covid-19 in such a circumstance.


To date, many vaccinations have been developed to combat covid-19. So far, no vaccine has guaranteed 100 percent accuracy, but most manufacturers promise that even if their vaccination isn't perfect, it will save your life by providing immunity.


As a result, each country attempts to vaccinate a significant proportion of its people in order to avoid relying on a single vaccine.



  1. Whatsapp chats analysis


With over 2 billion users globally, WhatsApp is one of the most popular messaging apps today. Because WhatsApp sends over 65 billion messages every day, we may utilise WhatsApp chats to analyse our conversations with friends, customers, or groups of people.


Many data science activities, including sentiment analysis, keyword extraction, named entity identification, text analysis, and other natural language processing tasks, may be performed with WhatsApp data. It also depends on who you're studying your WhatsApp chats with, because you may glean a lot of information from them, which can aid in the resolution of business issues.

the diagram shows the top 10 machine learning projects that can be carried out by enthusiasts. The projects are amazon product review analysis, hate speech detection, Covid-19 vaccine analysis, whatsapp chat analysis, Dogecoin price prediction, social media ads classification, spotify recommendation system, bankruptcy prediction model, instagram algorithm and netflix data analysis.

Machine learning project topics

  1. Dogecoin price prediction


The current decline in bitcoin prices is due to Dogecoin. Although Dogecoin is presently relatively inexpensive in comparison to bitcoin, some financial experts, like Tesla CEO Elon Musk, believe that the price of Dogecoin will soon climb.


In machine learning, predicting the price of a cryptocurrency is a regression issue. Bitcoin is one of the most successful instances of cryptocurrency, however owing to dogecoin, bitcoin values have lately plummeted. Unlike bitcoin, dogecoin is now relatively inexpensive, but financial analysts expect a significant increase in dogecoin values in the near future.


For the job of Dogecoin price prediction, we may utilise a variety of machine learning methods. You may either train a machine learning model or utilise a strong model that is currently available, such as the Facebook Prophet Model.


(Must read: Dogecoin vs Bitcoin)



  1. Social media Ads Classification


Classifying social media advertisements entails examining your ads in order to identify the most lucrative clients for your product, as well as those who are more likely to purchase it. When it comes to age and money, the product you're selling isn't always appropriate for everyone.


A person between the ages of 20 and 25 may, for example, choose to spend more on smartphone covers than someone between the ages of 40 and 45.


Similarly, a high-income individual may afford to spend more on luxury items than a low-income individual. So, by categorising their social media marketing, a corporation may assess whether or not a customer would buy their goods.



  1. Spotify recommendation system


The growth of music streaming services is exemplified by Spotify. The user experience that an app delivers to its users is crucial to its success. A recommendation system aids a streaming application's ability to provide a positive user experience. 


As a consequence, we can claim that the Spotify recommendation algorithm has played a significant part in creating a positive user experience, which has led to Spotify's success.


Users may listen to songs and podcasts recommended by Spotify's recommendation algorithm, which employs collaborative filtering. To create a better user experience, collaborative filtering suggests items or services based on similarities between users and the products or services.



  1. Bankruptcy prediction model


Bankruptcy is a state of bankruptcy in which a company or legal entity is unable to repay its debts to creditors. When a debtor files for bankruptcy, it is usually imposed by a court order.


The idea of bankruptcy is based on financial records. If you're a data science enthusiast who got into the field after a career in commerce, you should be aware of what bankruptcy is. The term "bankruptcy" refers to a scenario in which a firm or legal entity fails to pay its creditors' debts and eventually becomes insolvent.


We can train a model to forecast if a corporation or a legal person will go bankrupt in the future using machine learning techniques.



  1. Instagram algorithm


One of the most essential responsibilities for any business that depends significantly on social media consumers is predicting the reach of Instagram posts. In such a competitive environment, understanding how the Instagram algorithm works is critical.


What users see in their Instagram posts and stories is determined by a combination of user behaviour seen in the majority of the material they consume. The sort of posts people interact with, the type of posts they like, and the type of posts they engage in conversation are the most significant qualities that contribute to the reach of your Instagram posts.


When the following qualities in your posts provide Instagram's algorithm a favourable signal, an algorithm gives your post more chances to reach a larger audience.(Here)



  1. Netflix Data analysis


Netflix is one of the most popular internet streaming service providers. Because of its vast subscriber base, it collects a tremendous quantity of data.


Netflix has constantly focused on changing business demands by moving its business model from on-demand DVD movie rental to currently concentrating a lot on the development of their original series, so we can study a lot of data and models from them.


(Related reading: Netflix case study)


We'll take a look at some of Netflix's most significant data models to see what works best for them. The following are some of the most essential things that Netflix data may help us with:


  • understand what content is available

  • understand the similarities between the content

  • understand the network between actors and directors

  • what exactly Netflix is focusing on

  • and sentiment analysis of content available on Netflix.


In the end, these projects have been compiled from different aspects of society and are fun to work with as they are important functions in today’s sedentary life.

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