Machine Learning Project Ideas for Beginners

 

Are you a data scientist looking for certifications that will make you more employable in the industry? Do you want to prepare for various data scientist roles like a machine learning engineer? Prepare with a Machine Learning certification and watch your career grow.

Project ideas to get you started

In this blog, you will find 10 machine learning project ideas for beginners that will give you hands-on experience with real-world scenarios. These machine learning project ideas help you learn the various use cases needed to succeed in your career.

They can be developed in Python or R, or other tools.

Stock market prediction

Stock prices prediction is a machine learning beginner’s project aimed at predicting the future price of the stock market based on the previous year’s data. Technical analysis will use metrics like opening price, closing price, trading volume, and delivery, and qualitative analysis will use macroeconomic indicators like market status, company performance and financials, and so on. Simple projects to work on are the prediction of 6-month price movements from companies’ quarterly reports or building time series models based on implied and actual volatility.

Fake news detection on Twitter

Learn to distinguish fake news using supervised learning and ensemble approach for automated classification of text-based news. This project used NLP techniques and text mining to detect fake news and misleading content. The text classification approach is used to design a model that can differentiate between real and fake news. Datasets are collected for both real and fake news. A machine learning model is used with the Naive Bayes classifier to classify news content as false or real based on the words and phrases used in it.

Build an online grocery recommendation

Use collaborative filtering to recommend grocery items that a customer might like to order based on the reaction of similar users.

Loan prediction

Build a linear model to classify how much loan a user is eligible to take based on the user’s marital status, education, professional qualification, employment history, salary, number of dependents, and credit scoring.

Credit Card fraud detection

Create a fraud detection model on credit cards. Use transaction data, credit card history, and labels that mark new transactions made by the customer as fraud or non-fraud made to build a fraud detection model.

Customer segmentation

Create a customer segmentation model. Customer segmentation is a technique in which customers are categorized based on their purchase history, gender, age, interest, buying preferences, cart abandonment, etc. This information is used for personalized marketing campaigns, customized recommendations, and discounted offers to customers based on the segments or categories.

Sentiment Analysis

Practice a sentiment analysis project on the Twitter platform to understand the sentiments of a particular political campaign. Mine the data using keywords and computational linguistics to understand the pulse and public sentiment.

While there are many social media platforms to choose from, Twitter makes a good entry point for practicing machine learning.

On Twitter, you have access to a mix of data (tweet contents) and meta-data (location, hashtags, users, engagement, likes, re-tweets, etc.) that facilitate deeper analysis of the users’ emotions. The text mining will uncover trends and popular public sentiments for insights into the effectiveness of the political campaign. As a beginner, you can build a model to classify emotions as positive, negative, or neutral/uninterested.

Customer churn prediction using ensemble techniques

Customers are a company’s greatest asset. Very often, customers have not purchased any goods or services for some time. Companies want these customers to become repeat purchasers. The cost of acquiring a new customer is five times more than that of retaining an existing customer. Customer churn or attrition is one of the most acknowledged problems in the business where customers stop purchasing from a company.

There is customer churn if a specific time has passed since the customer last engaged or purchased something. The business may have lost the customer to another website or that the customer has lost interest. Sometimes a business may see a high customer churn or attrition in specific products. The company will want to predict the customer churn. You can develop a customer churn model using the ensemble method.

Machine learning methods identify factors leading to the churn and the behavioral patterns of customers who stopped buying the goods or services. The behavior of existing customers is checked against such patterns to identify potential churners. Feature Engineering is a part of the churn prediction machine learning model, where business context and domain knowledge are leveraged to create features and tailor the machine learning model to understand why customer churn happens.

App cab ride demand

Ride-hailing services or app cabs face different demands for rides during different times of the day and at various locations. Better forecasting at these ride-hailing services can help them manage surge pricing and send alerts to drivers based on the predicted demand for ride requests. It improves overall customer satisfaction. Undertake a machine learning project to predict the app cab demand for a rideshare company for a given location and time duration. Factors like the number of cabs, weather, and traffic, play a role in the demand forecast.

Sales prediction

Forecast the total amount of products that are likely to be sold in a particular month with current daily sales data and sales of last year during the same period. Build a regression model to predict the sales of the given products for the month in a particular outlet.

Sales prediction helps in inventory management and stock up with the required amount of goods. Meeting customer demands ensures customer satisfaction.

Store sales are affected by many factors like competition, holidays, season, changing preferences, etc. Identifying patterns in these trends can help to predict sales through the application of machine learning. This project is best for learning how unsupervised machine learning algorithms function.