You want to learn machine learning but are having trouble getting started with it. Books and courses might not just be enough when it comes to machine learning though they always give sample machine learning codes and snippets, you do not get an opportunity to implement machine learning to real-world problems and see how these code snippets fit together. The best way to get started with learning machine learning is to implement beginner to advanced level machine learning projects. It is always helpful to gain insights into how real people are beginning their careers in machine learning by implementing end-to-end ML projects.
In this blog post, you will find out how beginners like you can make great progress in applying machine learning to real-world problems with these fantastic machine learning projects for beginners recommended by industry experts. ProjectPro industry experts have carefully curated the list of top machine learning projects for beginners that cover the core aspects of machine learning such as supervised learning, unsupervised learning, deep learning, and neural networks. In all these machine learning projects you will begin with real-world datasets that are publicly available. We assure you will find this blog absolutely interesting and worth reading because of all the things you can learn from here about the most popular machine learning projects.
"What projects can I do with machine learning ?" We often get asked this question a lot from beginners getting started with machine learning. ProjectPro industry experts recommend that you explore some exciting, cool, fun, and easy machine learning project ideas across diverse business domains to get hands-on experience on the machine learning skills you've learned. We've curated a list of innovative and interesting machine learning projects with source code for professionals beginning their careers in machine learning. These beginner projects on machine learning are a perfect blend of various types of challenges one may come across when working as a machine learning engineer or data scientist.
Let's dive in!
Sales forecasting is one of the most common use cases of machine learning for identifying factors that affect the sales of a product and estimating future sales volume. This machine learning project makes use of the Walmart dataset that has sales data for 98 products across 45 outlets. The dataset contains sales per store, per department on weekly basis. The goal of this machine learning project is to forecast sales for each department in each outlet to help them make better data-driven decisions for channel optimization and inventory planning. The challenging aspect of working with the Walmart dataset is that it contains selected markdown events that affect sales and should be taken into consideration.
This is one of the most simple and cool machine learning projects where you will build a predictive model using the Walmart dataset to estimate the number of sales they are going to make in the future and here's how -
After working on this Kaggle machine learning project you will understand how powerful machine learning models can make the overall sales forecasting process simple. Re-use these end-to-end sales forecasting machine learning models in production to forecast sales for any department or retail store.
Want to work with Walmart Dataset? Access the Complete Solution to this awesome machine learning project Here – Walmart Store Sales Forecasting Machine Learning Project
BigMart sales dataset consists of 2013 sales data for 1559 products across 10 different outlets in different cities. The goal of the BigMart sales prediction ML project is to build a regression model to predict the sales of each of 1559 products for the following year in each of the 10 different BigMart outlets. The BigMart sales dataset also consists of certain attributes for each product and store. This model helps BigMart understand the properties of products and stores that play an important role in increasing their overall sales.
Access the complete solution to this ML Project Here – BigMart Sales Prediction Machine Learning Project Solution
This is one of the most popular machine learning projects and can be used across different domains. You might be very familiar with a recommendation system if you've used any E-commerce site or Movie/Music website. In most E-commerce sites like Amazon, at the time of checkout, the system will recommend products that can be added to your cart. Similarly on Netflix or Spotify, based on the movies you've liked, it will show similar movies or songs that you may like. How does the system do this? This is a classic example where Machine Learning can be applied.
In this project, we use the dataset from Asia's leading music streaming service to build a better music recommendation system. We will try to determine which new song or which new artist a listener might like based on their previous choices. The primary task is to predict the chances of a user listening to a song repetitively within a time frame. In the dataset, the prediction is marked as 1 if the user has listened to the same song within a month. The dataset consists of which song has been heard by which user and at what time.
Do you want to build a Recommendation system - check out this solved ML project here – Music Recommendation Machine Learning Project
The smartphone dataset consists of fitness activity recordings of 30 people captured through smartphone-enabled with inertial sensors. The goal of this machine learning project is to build a classification model that can precisely identify human fitness activities. Working on this machine learning project will help you understand how to solve multi-classification problems.
Get access to this ML projects source code here Human Activity Recognition using Smartphone Dataset Project
This is another interesting machine learning project idea for data scientists/machine learning engineers working or planning to work with the finance domain. A stock prices predictor is a system that learns about the performance of a company and predicts future stock prices. The challenges associated with working with stock price data is that it is very granular, and moreover there are different types of data like volatility indices, prices, global macroeconomic indicators, fundamental indicators, and more. One good thing about working with stock market data is that the financial markets have shorter feedback cycles making it easier for data experts to validate their predictions on new data. To begin working with stock market data, you can pick up a simple machine learning problem like predicting 6-month price movements based on fundamental indicators from an organizations’ quarterly report. You can download Stock Market datasets from Quandl.com or Quantopian.com. There are different time series forecasting methods to forecast stock price, demand, etc.
Check out this machine learning project where you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example. Stock Prices Predictor using TimeSeries Project
It’s a known fact that the older the wine, the better the taste. However, there are several factors other than age that go into wine quality certification which include physiochemical tests like alcohol quantity, fixed acidity, volatile acidity, determination of density, pH, and more. The main goal of this machine learning project is to build a machine learning model to predict the quality of wines by exploring their various chemical properties. The wine quality dataset consists of 4898 observations with 11 independent and 1 dependent variable.
Get access to the complete solution of this machine learning project here – Wine Quality Prediction in R
Deep learning and neural networks play a vital role in image recognition, automatic text generation, and even self-driving cars. To begin working in these areas, you need to begin with a simple and manageable dataset like the MNIST dataset. It is difficult to work with image data over flat relational data and as a beginner, we suggest you can pick up and solve the MNIST Handwritten Digit Classification Challenge. The MNIST dataset is too small to fit into your PC memory and beginner-friendly. However, handwritten digit recognition will challenge you.
Make your classic entry into solving image recognition problems by accessing the complete solution here – MNIST Handwritten Digit Classification Project
From Netflix to Hulu, the need to build an efficient movie recommender system has gain importance over time with increasing demand from modern consumers for customized content. One of the most popular datasets available on the web for beginners to learn building recommender systems is the Movielens Dataset which contains approximately 1,000,209 movie ratings of 3,900 movies made by 6,040 Movielens users. You can get started working with this dataset by building a world-cloud visualization of movie titles to build a movie recommender system.
Free access to solved code examples can be found here (these are ready-to-use for your ML projects)
Boston House Prices Dataset consists of prices of houses across different places in Boston. The dataset also consists of information on areas of non-retail business (INDUS), crime rate (CRIM), age of people who own a house (AGE), and several other attributes (the dataset has a total of 14 attributes). Boston Housing dataset can be downloaded from the UCI Machine Learning Repository. The goal of this machine learning project is to predict the selling price of a new home by applying basic machine learning concepts to the housing prices data. This dataset is too small with 506 observations and is considered a good start for machine learning beginners to kick-start their hands-on practice on regression concepts.
Recommended Reading - 15+ Data Science Projects for Beginners
Social media platforms like Twitter, Facebook, YouTube, Reddit generate huge amounts of big data that can be mined in various ways to understand trends, public sentiments, and opinions. Social media data today has become relevant for branding, marketing, and business as a whole. A sentiment analyzer learns about various sentiments behind a “content piece” (could be IM, email, tweet, or any other social media post) through machine learning and predicts the same using AI.Twitter data is considered as a definitive entry point for beginners to practice sentiment analysis machine learning problems. Using the Twitter dataset, one can get a captivating blend of tweet contents and other related metadata such as hashtags, retweets, location, users, and more which pave way for insightful analysis. The Twitter dataset consists of 31,962 tweets and is 3MB in size. Using Twitter data you can find out what the world is saying about a topic whether it is movies, sentiments about US elections, or any other trending topic like predicting who would win the FIFA world cup 2018. Working with the Twitter dataset will help you understand the challenges associated with social media data mining and also learn about classifiers in depth. The foremost problem that you can start working on as a beginner is to build a model to classify tweets as positive or negative.
Free access to solved code Python and R examples can be found here (these are ready-to-use for your Data Science and ML projects)
This is one of the most simple machine learning projects with Iris Flowers being the simplest machine learning datasets in classification literature. This machine learning problem is often referred to as the “Hello World” of machine learning. The dataset has numeric attributes and ML beginners need to figure out how to load and handle data. The iris dataset is small which easily fits into the memory and does not require any special transformations or scaling, to begin with.
Iris Dataset can be downloaded from UCI ML Repository – Download Iris Flowers Dataset
The goal of this machine learning project is to classify the flowers into among the three species – virginica, setosa, or versicolor based on length and width of petals and sepals.
Free access to solved machine learning Python and R code examples can be found here (these are ready-to-use for your projects)
Pricing races are growing non-stop across every industry vertical and optimizing the prices is the key to manage profits efficiently for any business. Identifying a reasonable price range and making an adjustment to the pricing of products to increase sales while keeping the profit margins optimal has always been a major challenge in the retail industry. The fastest way retailers can ensure the highest ROI today whilst optimizing the pricing is to leverage the power of machine learning to build effective pricing solutions. Ecommerce giant Amazon was one of the earliest adopters of machine learning in retail price optimization that contributed to its stellar growth from 30 billion in 2008 to approximately 1 trillion in 2019.
Image Credit: spd. group
The retail price optimization machine learning problem solution requires training a machine learning model capable of automatically pricing products the way they would be priced by humans. Retail price optimization machine learning models take in historical sales data, various characteristics of the products, and other unstructured data like images and textual information to learn the pricing rules without human intervention helping retailers adapt to a dynamic pricing environment to maximize revenue without losing on profit margins. Retail price optimization machine learning algorithm processes an infinite number of pricing scenarios to select the optimal price for a product in real-time by considering thousands of latent relationships within a product.
Check this cool machine learning project on retail price optimization for a deep dive into real-life sales data analysis for a Café where you will build an end-to-end machine learning solution that automatically suggests the right product prices.
Customers are a company’s greatest asset and retaining customers is important for any business to boost revenue and build a long-lasting meaningful relationship with customers. Moreover, the cost of acquiring a new customer is five times more than that of retaining an existing customer. Customer Churn/Attrition is one of the most acknowledged problems in the business where customers or subscribers stop doing business with a service or a company. Ideally, they stop being a paid customer. A customer is said to be churned if a specific amount of time has passed since the customer last interacted with the business.
Identifying if and when a customer will churn and quickly delivering actionable information aimed at customer retention is critical to reducing churn. It is not possible for our brains to get ahead of customer churn for millions of customers, this is where machine learning can help. Machine learning provides effective methods for identifying churn’s underlying factors and proscriptive tools for addressing it. Machine learning algorithms play a vital role in proactive churn management as they reveal behavioral patterns of customers who have already stopped using the services or buying products. Then, the machine learning models check the behavior of the existing customers against such patterns to identify potential churners.
Image Credit. :gallery.azure.ai
But how to start with solving the customer churn rate prediction machine learning problem? Like any other machine learning problem, data scientists or machine learning engineers need to collect and prepare the data for processing. For any machine learning approach to be effective, engineering the data in the right format makes sense. Feature Engineering is the most creative part of the churn prediction machine learning model where data specialists use their experience, business context, domain knowledge of the data, and creativity to create features and tailor the machine learning model to understand why customer churn happens in a specific business.
Image Credit: medium.com
For example, in the Banking industry, two accounts that have the same monthly closing balance can be difficult to differentiate for churn prediction. But, feature engineering can add a time dimension to this data so that ML algorithms can differentiate if the monthly closing balance has deviated from what is usually expected from a customer. Indicators like dormant accounts, increasing withdrawals, usage trends, net balance outflow over the last few days can be early warning signs of churn. This internal data combined with external data like competitor offers can help predict customer churn. Having identified the features, the next step is to understand why churns occur in a business context and remove the features that are not strong predictors to reduce dimensionality.
Check out this end-to-end machine learning project with source code in Python on Customer Churn Prediction Analysis using Ensemble Learning to combat churn.
No project advances successfully without solid planning, and machine learning is no exception. Building your first machine learning project is actually not as difficult as it seems provided you have a solid planning strategy. To start any ML project, one must follow a comprehensive end-to-end approach -starting from project scoping to model deployment and management in production Here’s is our take on the fundamental steps of a machine learning project plan to ensure that you make the most of each unique project –
Before anything else, understand what are the business requirements of the ML project. When starting an ML project selecting the relevant business use case the machine learning model will be built to address is the fundamental step. Choosing the right machine learning use case and evaluating its ROI is important to the success of any machine learning project.
Data is the lifeblood of any machine learning model and it is impossible to train a machine learning model without data. The data stage in the lifecycle of a machine learning project is a four-step process –
Data Requirements – Understanding what kind of data will be needed, the format of the data, the data sources, and compliance requirements of the data sources is important.
Data Collection – With the help of database admins, data architects, or developers you need to set up the data collection strategy to extract data from places where it lives within the organization or from other third-party vendors.
Exploratory Data Analysis – This step basically involves validating the data requirements to ensure that you have the correct data, the data is in good condition, and free from errors.
Data Preparation – This step involves preparing the data for use by machine learning algorithms. Error correction, feature engineering, encoding to data formats that machines can understand, and anomaly correction are the tasks involved in data preparation.
Depending on the nature of the project, this step might take a few days or months. In the modeling stage, you take a decision on which machine learning algorithm to use and start training the model on the data. Understanding the measure of accuracy, error, and correctness a machine learning model should adhere to is important for model selection. Having trained the model, you evaluate it on validation data so analyze its performance and prevent overfitting. Model evaluation is a critical step because if a model works perfectly with historical data and returns poor performance with future data, it’s of no use.
This step involves deploying software or app to end users so new data can flow into the machine learning model for further learning. Deploying the machine learning model is not enough, you also need to ensure that the machine learning model is performing as expected. You should retrain your model on the new live production data to ensure its accuracy or performance- this is model tuning. Model tuning also requires validating the model to ensure that it is not drifting or becoming biased.
Real-world experience prepares you for ultimate success like nothing else. As a machine learning beginner, the more you can gain real-time experience working on machine learning projects, the more prepared you will be to grab the hottest jobs of the decade. Getting a machine learning job after completing data science training or becoming successful as a data scientist will depend on your ability to sell yourself. Having taken comprehensive data science training, the next step to land a top gig as a machine learning engineer or a data scientist is to build an outstanding portfolio to showcase your ability to apply machine learning techniques to your prospective employers. Working on interesting ML projects is a great way to kick-start your career as an enterprise machine learning engineer or data scientist. Employers want to see what kind of projects related to data science and machine learning you have worked on to evaluate the range of your abilities in doing data science and machine learning. Highlighting some fun, cool, and interesting data science and machine learning project examples on your resume will carry more weight than telling them how much you know. Here's how you can add awesome projects to your machine learning resume -
Whether you want to build up a strong machine learning portfolio or you want to practice analytic skills that you learned in your data science training course, we have got you covered. Many machine learning beginners are not sure where to start, what machine learning projects to do, what machine learning tools, techniques, and frameworks to use. We have made it a hassle-free task for data science and machine learning beginners by curating a list of interesting ideas for machine learning projects along with their solutions. These machine learning projects ideas are taken from popular Kaggle data science challenges and are a great way to learn machine learning. This list of projects is a perfect way to put machine learning projects on your resume. The right mindset, willingness to learn, and a lot of data exploration are all required to understand the solution to projects on data science and machine learning. You can explore 50+ data science and ML projects based on the set of skills, tools, and techniques you need to learn.
Before you get started on your project, it is helpful to have access to a library of machine learning project code examples. So anytime you are stuck on the project you can use these solved examples to get unstuck.
One can become a master of machine learning only with lots of practice and experimentation. Having theoretical knowledge surely helps but it’s the application that helps progress the most. No amount of theoretical knowledge can replace hands-on practice. However, it will help if you familiarize yourself with the above-listed innovative machine learning projects first.
If you are a beginner and new to machine learning then working on machine learning projects designed by industry experts at ProjectPro will make some of the best investments of your time. These projects have been designed for beginners to help them enhance their applied machine learning skills quickly whilst giving them a chance to explore interesting business use cases across various domains – Retail, Finance, Insurance, Manufacturing, and more. So, if you want to enjoy learning machine learning, stay motivated, and make quick progress then ProjectPro’s interesting ML projects are for you. Plus, add these machine learning projects to your portfolio and land a top gig with a higher salary and rewarding perks.