"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.
Aspiring machine learning engineers want to work on ML projects but struggle hard to find interesting ideas to work with, What's important as a machine learning beginner or a final year student is to find data science or machine learning project ideas that interest and motivate you. When deciding on a machine learning project to get started with, it's up to you to decide the domain of the dataset based on your interest, the complexity of the dataset, and the size of the dataset. To begin building your machine learning portfolio, you will need some cool, fun, and innovative machine learning project ideas to start working on. To get started with your data science or machine learning portfolio, brainstorm all possible ML project ideas that interest you. Once you have gathered a couple of beginner machine learning project ideas for 2021, you can choose the most interesting project ideas and get started working on those to add those machine learning projects to your resume. However, if you are a beginner or a student, ProjectPro experts recommend you get started with ML projects that focus on data cleaning and then move on to analytics.
Consider a situation, where you want to buy a house or sell a house, or you are moving to a new city and want to rent a house, but you don’t know where to start. Sometimes, it happens that you know where to start, but you doubt the credibility of the source. Well, some people from Microsoft also felt the need of creating a reliable place that can provide all this information online, and “Zillow” was born in 2006. A few years later, Zillow introduced a feature called “Zestimate”, which has completely changed the market. Zestimate is a tool that provides the worth of the house based on various attributes like public data, sales data, etc. Zestimate has information of more than 97 million homes.
Zestimate is the first step to analyze the worth of a house or to check if the value has been appraised or not after newly upgrading your home, or maybe you just want to refinance it. The algorithm behind Zestimate gets its data 3 times a week, on the basis of comparable sales and publicly available data. As per Zillow, Zestimates are within the range of 10% of the selling price of homes. By providing the approximate value ranges of the properties, Zillow balances the inaccuracy in the pricing, We can assume that the smaller the range, the more accurate will be the estimated price of the property, this is due to the fact, that Zillow will be having more data for that property. Using Zestimate, users can guess their home’s worth by checking the boundary values.
In this Machine Learning project for final year students, you will use the Zillows Economics dataset to build a house price prediction model with XGBoost based on factors like average income, crime rate, number of hospitals, number of schools, etc. Having completed this top ML project one should be able to answer questions like top States with highest rent Values, in which state should you buy/rent a house, Zestimate per square feet, median rental price for all homes, etc.
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
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.
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.
A time series is an analysis of event occurrences over a period of time. A time series is analyzed to identify patterns so that future occurrences can be predicted based on trends observed over a period of time. A time series is a good way to get an idea of seasonal variation, repetitive patterns and even to identify unexpected events to further understand what could have caused them. To perform time-series forecasts, there are various models that can be used. The selection of the model itself is dependent on various factors which include: the availability of the past data, the context of the forecast, the time period for which the forecast has to be made, and the time available to create the model and make the forecast. Some of the models which can be used for time series forecasting are moving-average, exponential smoothing, and ARIMA (autoregressive integrated moving average) model. The moving average model is a very straightforward modeling technique that predicts the next occurrence to be the mean of all the past occurrences. Although it seems very simple, it has been found to be quite accurate in many places. In the case of exponential smoothing, the mean is calculated by giving less weightage to occurrences that are further away from the present. This means that more recent occurrences have more value towards the calculation of the mean than older events. The ARIMA model is a slightly more complex model. It is a form of regression analysis that monitors the strength of one dependent variable based on other changing variables.
Check out this machine learning project where you will learn to determine which forecasting method to be used when and how to apply it 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.
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.
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 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.
Coupon Marketing is a strategy used by businesses to lure customers to buy their products. Coupons are an easy and very commonly used strategy that can be used across several domains for discounts and promo codes. Apart from the usual e-commerce sites, coupons would even be beneficial in the travel industry for discounts on flights and hotel bookings, in the health sector for discounted consultations, and even on educational platforms so that expected clients can get an idea of the business. This marketing strategy will be the most useful only if it reaches the intended audience. By analyzing the reaction of customers to different kinds of coupons, it is possible to determine their future behavior and interest in various coupons. Since many times when a customer receives a coupon, it gives the feeling of having received a deal from the business, coupons help to increase customer loyalty. For new consumers, coupons are a form of fresh exposure to a new product or service and give the consumer more reason to try something new. This can help to have a competitive edge over other businesses in the same field. Machine learning tools and techniques can be applied to analyze customer usage behavior for various coupons and in that manner, perform coupon purchase prediction. This helps generate a better recommendation system so that coupons can be generated more specifically to various customers.
Loans are what make the world go round. They are the core business for banks since their main profit comes from interest on loans. Economies can only grow when an individual or a group of individuals invests some amount of money in a business, in the hope that it can multiply in value in the future. Sometimes, to be able to take risks of this sort and sometimes, even to have some worldly pleasures, it becomes necessary for one to apply for a loan. Banks usually have a very rigorous process to be followed before a loan can be approved. Since loans form such an important part of many of our lives, it would be very helpful to predict the eligibility for a loan that someone applies for, so that there can be better planning beyond the loan being approved or rejected. The model for determining loan eligibility prediction has to be trained using a dataset that consists of data including data such as sex, marital status, number of dependents, income, qualifications, credit card history and loan amount to name a few. For this project, we make use of the dataset from SYL bank. The SYL bank is one of Australia’s largest banks. This project will involve training and testing the data model using the method of cross validation. The data will have to be cleansed and missing values filled in. This project is an excellent means to learn how to build statistical models such as Gradient Boosting and XGBoost, and also to understand metrics such as ROC Curve, MCC scorer and the like.
As the coronavirus hit the world in 2020, shopping stores have been pushed to take their business online as customers are gradually considering online shopping. But, customers are still looking for exciting deals as they did in stores and thus, they are increasingly searching for super saving coupons. And, there are now special websites that make coupons for such customers.
One such website in Japan is Recruit Ponpare that offers great discounts for yoga, gourmet sushi, and even for a summer concert bonanza. Using the shopping behaviour of customers in the past, you can do a machine learning project that enhances the Ponpare’s recommendation system. The recommendation system’s task is to estimate which coupons the customer is most likely to purchase in a given period of time on the basis of previous shopping behaviour of the customer.
Through this project, you can introduce yourself to the idea of data munging in machine learning, plotting bar plots, pie charts and histograms to visualise data, and feature engineering. You can also explore data imputation techniques for handling NA values and cosine similarities of variables to make predictions. If all these words sound too technical to you and you don’t know where to start, check out Build a Coupon Purchase Prediction Model in R, a project from our repository that will guide you through complete implementation of this machine learning project.
Zomato is a popular mobile application in India that connects its customers to nearby food chains by providing their own delivery persons. Recently, on 10 July 2021, Zomato completed its thirteen years of existence and has launched a campaign, ‘No Cooking July’ to celebrate this feat. The company has planned to launch exciting offers daily for its customers as a part of the campaign. These offers are definitely being enjoyed by the customers as they are getting yummy food at good prices. But, the restaurants are facing challenges as they have to make sure to cater as many customers as possible. For such cases, it becomes important for the food outlets to prepare their Inventory accordingly.
Preparing sufficient inventory is a task that not only restaurants registered on Zomato have to complete. Most companies that offer products have to make sure that they have enough to satisfy all their customers. It thus becomes important to have a rough estimate of how much preparation would be enough. This estimation can be achieved by what we call, demand forecasting. A demand forecast is vital for planning all business decisions: sales, finance, production management, logistics and also marketing. If these forecasts are correctly predicted, they can help the businesses grow significantly by allowing them to reach their customers with the right products at the right time. It can also help the businesses in avoiding unnecessary wastage of their resources.
These predictions in demand forecasting can be made through the application of relevant machine learning algorithms. This machine learning project can be implemented by utilizing machine learning algorithms like Bagging, Boosting, XGBoost, Gradient Boosting Machine (GBM), Support Vector Machines, and many more. If these algorithms sound new to you and you have no idea how to use them for real-world applications, don’t worry at all because we got the perfect solution for you, read our Inventory Demand Forecasting using Machine Learning in R project that will help you.
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.
With a versatile machine learning project repository, 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 project ideas for beginners recommended by industry experts. ProjectPro industry experts have carefully curated the list of top machine learning projects for beginners with source code 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 these ML projects absolutely interesting and worth practicing because of all the things you can learn from here about the most popular machine learning tools and techniques.
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.
end-to-end machine learning project with source code in Python on Customer Churn Prediction Analysis using Ensemble Learning to combat churn.
The aim of this ML project is to predict customers who will default on a loan. The banks may experience loss on the credit card product from various sources and one possible reason for the loss is when customers default on their debt preventing banks from collecting payments for the services rendered. In this machine learning project, you will examine a slice of the customer database to find out how many customers will be seriously delinquent in making payments in the next 2 years. There are various machine learning models for predicting which customers default on a loan so the banks can cancel credit lines for risky customers or decrease the credit limit on the card to minimize losses. These models will also help banks screen which customers can be approved a credit card.
Dataset – Give Me Some Credit Kaggle Dataset
Ride-hailing services like Uber, Ola, and Lyft have become an integral part of urban transportation worldwide. Better forecasting at these ride-hailing services can help them reduce surge pricing, benefit them with overall city traffic planning, send alerts to drivers based on the upcoming demand for ride requests, and improve overall customer satisfaction with better services. At Ola, choosing the right forecasting methodology for a use case like bike ride request demand is dependent on several factors like how much data is available, the business requirements, and other external factors such as weather play a vital role. In this machine learning project, you will choose the best machine learning approach to predict Ola bike ride request demand for a given latitude and longitude for future time duration.
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
After a long day of work, we all look forward to going back to our homes and getting some comfort in those familiar walls. Even more so now, with the pandemic that has changed the work culture and encouraging more of us to work from home, the importance of finding a house that is cozy and accommodating has become a matter of utmost importance. Going through long lists of options on rental sites can be very tiring and can result in one settling for a house that is not up to the mark. By performing a sentimental analysis on the viewers for various rental listings, it is possible to determine their reactions towards certain houses and accordingly, understand the popularity of houses that are up for rent. This can further help to predict the interest levels of new places that are to be listed. This knowledge is beneficial to the owners as well so that they can plan ahead based on the predictions for the number of inquiries expected. The challenge here is to group the past data and make sense of it. In this manner, it will allow for better handling of fraud control, identify potential quality issues or concerns that may arise while listing, and also help the owners and agents to get a better idea of what attracts renters.
Ride sharing and food delivery services across the globe rely on the availability of drivers to operate smoothly. Predicting the availability of drivers in a particular locality so that the users have information on whether a cab would be arriving or not and what would be the tentative waiting time for the arrival. This helps efficiently allocate drivers to locations where there is demand.To predict the driver demand, in this ML project we will convert a time series problem to a supervised machine learning problem. Exploratory analysis has to be performed on the time series to identify patterns. Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) will be applied to analyse the time series. A regression model will have to be built and used to solve this time-series problem. Once the training model is prepared, spot testing will be performed on it. Following this, prediction of driver demand will be performed by making use of Random Forest and Xgboost as the ensemble models.
Learn to apply multi step time series analysis to predict driver demand- Access Solution to Driver Demand Prediction ML Project
The speed at which data travels has drastically increased. Gone are the days when letters had to be sent before news could reach from one person to another. With the emergence of the internet, it has become possible for family and friends from across the globe to stay in touch with each other and always be updated with what’s happening on the other side of the world. Similarly, even news seems to be travelling at lightning speed now. This has proven to be helpful in many situations. However, just like how the internet has helped us to react to news and emergencies much faster, it has also resulted in the emergence of unwanted spread of misinformation across platforms. As opposed to previously where articles were checked multiple times by editors, and the source of news could easily be traced, now people are relying on social media platforms, blogs and other news platforms online for news. And since it is so easy to write anything on the internet and just send it across, fake news has become very common.
Fake news can be of the following types:
linguistics-based news, which consists of news in the form of text, or a string of characters
Graphics-based news, which consists of data in the form of images, video or any other graphic representation.
Due to the sheer volume and speed of data across the internet, it is not possible to take every news clip and have it analysed by an expert. Hence, a technique to determine fake news by applying methods based on Natural Language Processing are proposed to identify fake news in real-time and prevent the spread of misinformation.
Market basket analysis refers to the process of better understanding combinations in which customers often purchase various commodities. It is a data mining technique which is used to observe purchasing patterns in consumers to better understand them and in the process, increase sales. The idea here is that if a customer purchases an item or a group of items, say product ‘A’, then this increases the chances that the customer would also be interested in purchasing another item or another group of items, ‘B’; An interest in A implies an interest in B based on the behaviours of previous customers. Market Basket Analysis can be used for targeted promotions, personalised recommendations for customers and for cross-selling. For example, offering a discount on a product ‘B’ for a customer who purchases ‘A’, or advertising A and B together. Even menus can be written up keeping in mind the results drawn from market basket analysis. In grocery stores, the aisles can be arranged according to products which are observed to be purchased together frequently. Market basket analysis can help improve sales for a business, but can also be beneficial to customers, since in some cases some buyers may have forgotten to purchase item B along with item A.
Access Solution to Market Basket Analysis
Do you remember that scene from the movie Titanic (1994) wherein the end an officer is making a list of who survived after the ship sank? In case you don’t, please feel free to watch it again here. The tragic accident happened in 1912 and there were only about 1500+ that could have their names on that list.
Now, if you are wondering how all that is related to a Machine Learning project, don’t be surprised by knowing that Kaggle actually has a very popular challenge related to the Titanic ship. The task is to predict which passengers on the ship will survive given their name, age, gender, socio-economic status, etc. You can use any machine learning model that you like to model the given dataset and figure out which best correlates the passenger characteristics to the chances of their survival on the ship.
If you are a beginner in Data Science, then this project is a must for you. For the ways in which you can implement this project, you can of course do a quick google search but in case you are interested in a one-stop solution, check out this machine learning project: Kaggle Data Science Challenge -Predicting survival on the Titanic from our repository.
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
Topic modeling is an unsupervised machine learning technique for text analysis. Topic Modelling helps organizations garner valuable insights from data by understanding the likes and dislikes of customers, find a theme across product reviews, analyze online conversations, etc. Let’s say you work for a retail brand like Armani and you want to understand what customers have to say about the specific features of your fashion products. Rather than spending hours scrolling through the customer reviews to understand which reviews are talking about your topics of interest (products), it would be much easier to analyze them with a topic modeling machine learning algorithm. This kind of analysis helps businesses focus on further improvements and prepare for the future. By detecting patterns like the distance between words, the frequency of words, a topic modeling algorithm will group similar feedback and expressions that appear most often to help deduce what customers are talking often about.
This Natural Language Processing Project uses the RACE dataset for the application of Latent Dirichlet Allocation(LDA) Topic Modelling with Python. RACE is a big dataset of more than 28K comprehensions with around 100,000 questions. Each document in the dataset will be made up of at least one topic, if not multiple topics.
Dataset: RACE Dataset
Income inequality has been of great concern in recent years and census data can be of great help in predicting data like the health and incomes of every individual based on historical records. The goal of this machine learning project is to use the adult census income dataset to predict whether income exceeds 50K yr based on census data like education level, relationship, hours of work per week, and other attributes. The Adult Census Income dataset is interesting because of its richness and diversity of data right from the education level of a person to their relationship level. With over 32K rows and a total of 15 columns describing various attributes of people- Adult Census Income Dataset is a perfect blend of missing values, numerical, and categorical data making it a great choice for building a classifier.
Dataset – Adult Census Income Dataset
The pandemic has compelled each one of us to analyze emotions in communication, as all we are left with today is virtual communication. Thus, it becomes a herculean task to detect correct emotions. There is no definitive way to determine the emotions from speech and hence, the Speech Emotion Recognition(SER) system was defined, which is a combination of different frameworks and works on the basis of analyzing audio signals to identify emotions. In general, a human brain separates emotions from a speech by dividing speech into 3 parts, the acoustic part, the lexical part, and the vocal part. We can use one or combine other parts to reach the correct emotion, but in this fun machine learning project, we will be using the acoustic part of speech which includes pitch, jitter, tone, etc.
A surgical procedure is no joke. There are risks and complications involved not to mention the post-surgery recovery. Post-surgery pain is also an issue that many patients have to face. Currently, pain in adults is managed by using medicines, which have their own set of side effects. By using ultrasound nerve segmentation, the source of the pain can be found and the pain can be treated at the source rather than with drugs which will only temporarily numb the pain. Accurate identification of nerve structures in ultrasound images can help in determining the source of the pain and accordingly inserting a catheter for better pain management. The nerve structures have to be analyzed as accurately as possible since this analysis deals directly with a patient and lives are at stake. Mistakes, which can lead to incorrect insertion can result in more problems for the patients later on. This project involves gathering images that contain nerves that do not show any signs of damage to compare them with nerves that show signs of abnormality, which could be indicative of pain. Images will have to be broken down into a matrix for analysis.
Avocados seem to be increasingly popular among millennials. It was observed that over 2.6 billion pounds of avocado were consumed in the United States alone in 2020, as opposed to only 436 million pounds consumed in the year 1985, as per Statista. Avocados are seen as a healthy option and are popular for being a good source of “good fats”. The fruit can be spread on toast, eaten raw, or even consumed in the form of a shake. Guacamole, which is a Mexican dip, is also made from avocados. Like most other products, the price of avocados fluctuates based on season and supply, which is why it would be beneficial to have a machine learning model to monitor and predict avocado prices. More awareness of the sales and prices of avocados can benefit the vendors, producers, associations, and companies. Price prediction based on sales would be a good input in the market to determine shifting of produce to locations where the fruit is more in demand or even encouragement of consumption in places where demand is not up to the mark. The idea here is to predict future prices based on data collected of past prices based on geographical location, weather changes, and seasonal availability of avocados.
According to Investopedia, a time series is a sequence of data points that occur in successive order over some period of time. The idea of time series analysis is to look at data characteristics over a certain time period and use that to make futuristic calculations. This means that future events may be predicted by taking into consideration previous events that have repeatedly occurred over a particular time period or occur due to certain other phenomena by analyzing a time series. Time Series Analysis is done to find hidden patterns in the data. These hidden patterns can be due to certain trends or it can be found that there is a seasonal variation in the patterns. The analysis can also help to identify anomalies in the data by observing unexpected occurrences and determining what has caused them. While observing a time series, certain patterns in event occurrence may be observed which can be used to classify the series. Modeling is usually done taking this classification into account. There are several models that can be used to perform time series forecasting. This is an advanced machine learning project in which time series modeling is done using Prophet, an open-source forecasting tool built by Facebook.
Access Solution to this Advanced Time Series Project with Facebook Prophet.
Quite often we see a pair of footwear that we like and want to buy, or maybe even a kitchen appliance that we do not recognize immediately but want to buy, maybe because it appears to be convenient. With the popularity of e-commerce, it has become very convenient to order items at the click of a button sitting in the comfort of our homes. However, in such cases, we need to at least know the name of the item that we want to purchase. It would be even more convenient if we could see something that we like, just click a picture and then find similar images of the item on e-commerce sites. This is one of the objectives of this interesting machine learning project. The goal here is to click a picture and be presented with more pictures that match the content in the original picture. It is important in this project for the system to accurately recognize products based on the image. The model has to be trained to identify and detect similar images so that the final model can pick up images that match the original image automatically and as accurately as possible.
Recruiters from companies and HR’s tend to have a tough time going through many resumes whenever there is a job opening. In cases of job roles that are high in demand, a large number of job applications come flowing in. Sometimes in the process of skimming through resumes, there is a possibility that an ideal candidate’s resume does not receive the necessary attention or maybe it is simply missed due to the huge pile of applications. This makes things difficult for both the job applicants and the company that they would have been more suited to be working in. This is a good application for machine learning, wherein it can be used to help in browsing through resumes. Using machine learning in such a scenario can not only reduce manual labor but also increase efficiency. A resume parser can be built to parse the required fields and categorize the applicants based on their resumes. Building a resume parser tends to get challenging since there are many different layouts followed by individuals. Each block of information would ideally be assigned a label and then be sorted into a corresponding category such as work history, education, qualifications, or even contact information. The lack of fixed patterns in such a scenario adds to the challenge.
Good inventory management is primarily about managing demand and supply. Having a good idea of the store sales can help to get a good idea of the demand for various products in the market and hence, stock up with the correct amount of goods. This is especially critical in terms of perishable goods since these goods have to be sold from stores before the end of their shelf life, otherwise, they will be wasted and also be a loss for the stores. Even in the case of non-perishable goods, it is important to have stock that is close to the amounts that will be sold, since many other products can go out of style too. Meeting the demands of customers ensures that customers too are kept satisfied. Many of us know how disappointing it can be to go to a store in search of a product only to realise that it is out of stock. Store sales can be influenced by many factors, some of which are: promotions, the presence of competitors, holidays, seasonality and locality. Identifying patterns in these trends and determining how they influence sales can be done through the application of machine learning.
Here is an example of a Store Sales Projection done for Rossman Stores.
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 project 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. Every organization has a different requirement to solve a specific business problem and it is your responsibility as a data scientist or machine learning engineer to adapt and deliver them a performance efficient machine learning solution. This will require rock-solid hands-on practice and experience working with diverse data science tools and machine learning technologies. So, what is the best way to master novel machine learning tools and technologies? Implement diverse end-to-end projects on your own. ProjectPro offers you some of the most interesting and cool machine learning projects that are implemented using novel machine learning tools and technologies.
So, if you are a final year student or a machine learning beginner, gearing yourself up with machine learning skills together with ProjectPro is definitely a kickass move now. 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.
Understand the overall machine learning process by identifying the business use-case, gathering data from various sources, and identify the machine learning algorithms used to solve the business problem.
Identifying the key functions needed to build the machine learning architecture in order to execute the machine learning project. This involves ingesting data from various sources, preparing ingested data for execution by including modules for data transformation, data cleansing, and data normalization, modeling the data and customizing the algorithms for the needs of the business, and executing the various machine learning modules.
The final step is to enable businesses to make the best use of the machine learning model in their own applications, data stores, or enterprise systems. The output of a machine learning project can be in the form of a report for profitable decision-making or information that can be used by other systems within the organization or a model that supports other analytic applications within the organization to garner valuable insights.