How is ML being used in some very real, day-today life activities? Read more to find out and gain a fresh perspective on ML deployment.
In this article, we will be walking you through 7 Fresh Machine Learning Project Ideas for Beginners. It will always be helpful to gain insights on how real people are beginning their careers in AI-based Machine Learning technology. With this you will learn to make great progress in applying ML to real-world problems with Machine Learning solutions.
Let’s learn about 7 Fresh Machine Learning Project Ideas:
1. Personality Prediction Project:
Do you also find it interesting to read the posts written by people online and be able to understand their overall personality?
With ML-based Personality Prediction Project, a lot of confusions on the internet would get solved about the author’s personality. The project is based on identifying the personality of an individual using ML algorithms and big 5 models. As we all know, the personality of a human plays a major role in his/her personal and professional life. The Big Five Model is also known as the Five-Factor Model (FFM) or OCEAN – which was developed in the early 80s. When the statistical analysis is applied to personality survey data, there are definitions – that are used to describe the person and altogether they provide you with a summary of the overall personality of the person.
- Open to Experience: It involves dimensions like imagination, sensitivity, attentiveness, preference to variety and curiosity.
- Conscientiousness: It involves carefulness, diligence, and the quality of how organized and efficient a person is.
- Extraversion: It describes how a person/candidate can interact with people – how good are his/her social skills.
- Agreeableness: It helps in analysing the individual behaviour based on the generosity, sympathy, cooperativeness and ability to adjust with people.
- Neuroticism: It will help in describing about the person’s mood swings and extreme excessive power.
2. Xbox Game Prediction Project:
Xbox? Do you like to play? Yes, we know, you love to play.
Gaming tools like Xbox games, come with lot of options for the consumers to choose from. The aim is to predict which Xbox game a person will be most interested in based on their search queries online.Companies like BestBuy, a consumer electronic company – which provides data on the search queries of millions of consumers to understand what Xbox game they might be interested in. The data contains their user ID, the item that user clicked on, the category the item belongs to the query, click time, and query time.
3. IMDB Box Office Prediction:
Movies are a big part of our world! But no one knows how a movie will perform at the box office. At times, even if it’s a big budget movie – it might not be a hit and a small budget movie might do wonders. With the help of this project, the researchers try to predict the overall worldwide box office revenue of movies using data such as the movie cast, crew posters, plot keywords, budget, production companies, release dates, languages and countries. And if you summarize all the points and prepare a report out of it, you will be able to predict how a movie will fare at the box office.
4. Credit Card Fraud Detection Project:
“The number of credit card owners is projected close to 1.2 billion by 2022.”
To ensure security of credit card transactions, it is essential to monitor fraudulent activities. Credit card companies should be able to recognize fraudulent credit card transaction so that customers don’t get charged for the transactions they have not done. A dataset of credit card contains a mix of fraud as well as non-fraudulent transactions and the target is to predict if a given test transaction is fraudulent or not.
With the help of algorithms like logistic regression, decision trees and neural network, the problem can be solved.
5. Customer Segmentation Project:
What is customer segmentation? It is a process of splitting a customer base into multiple groups of individuals – that share a similarity in ways a product is or can be marketed to them – like gender, age, interests, demographics, economic status, geography, behavioural patterns, spending habits and much more.
Customer Segmentation is one of the most important applications of unsupervised learning. With the help of cluster techniques, companies can identify the several segments – whom they can target as the potential user base. It will result into foresee or map customer segments with similar behaviour to identify and target potential user base.
Researchers or companies can take a help of algorithms like K-means clustering, hierarchical clustering, partitioning method, fuzzy clustering, density-based clustering and model-based clustering.
6. Sentiment Analysis Project:
How the sentiment analysis works? It is defined as a view of or attitude towards a situation or event. With the help of sentiment analysis, you can figure out the nature of opinion reflected in documents, websites, social media timelines, etc. Humans tend to have a range of sentiments from happy to sad, from positive to negative, from angry to calm, from love to hatred, from depression to irritation and many more.
In today’s time, any data-driven organization would imbibe outcomes from sentiment analysis model to determine the attitude of its customers and target customers towards the products and services. For instance, the social media platforms like Twitter, Facebook, Instagram, they run a sentiment analysis all the time. They perform sentiment analysis to understand the meaning and sentiment behind particular posts and comments.Are they portraying any negativity, nudity, harassment, or other kind of exploitation through their posts?
Researchers or companies can use the algorithms like Naïve Bayes, Decision trees and Package Tidytext to understand the meanings behind every posts and comments users are making.
7. Speech Emotion Recognition Project:
Speech Emotion Recognition Project can be a compelling Machine Learning + Data Science project to work on. It helps in perceiving human emotions from the speech (voice samples) – to sight human emotion, different sound files are used as the dataset. They are basically used to extract emotions from audio recordings. With the help of technologies like Python, Librosa (a tool to analyse music and audio), you can develop a tool or an app for SER.
Researchers or companies can use algorithms like Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Neural Network (NN), Gaussian Mixture Model (GMM) or Support Vector Machine (SVM).
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