10 Best Machine Learning Projects with datasets
In this blog, we will be discussing the 10 Best Machine Learning Projects with datasets that you need to work on as beginners to make your awesome portfolio in data science.
Machine Learning is one of the most popular technologies at present. It is transforming each and every industry drastically, be it E-Commerce, Healthcare, Finance, Security, etc.
“Machine Learning is a subset of Artificial Intelligence that provides a machine the ability to learn automatically and improve from experience without being explicitly programmed”.
Do you understand the Machine Learning concepts? And are you confused about how to progress further? Well, it is often said that the best way to learn any technology is by doing some projects. Projects are the best way to learn. Why? Because you get to implement all the theoretical concepts that you have learned. Other options like online courses, reading books, blogs, etc. only help in understanding the basics of ML, but it is only possible to truly learn the subject by doing projects with real-world data. By doing projects, you also get to know the probable errors that can happen and their solutions. During interviews, the company focuses a lot on the projects done by the candidate.
You must focus on building end-to-end Machine Learning Projects. For Instance, try to integrate your Machine Learning App with a Website and a Database too. You can also try integrating with MLOps tools like Docker, Kubernetes, MLFlow, etc. Having a solid Machine Learning Project would surely give you an edge over others in the interview.
In this particular blog, we will be discussing the 10 Best Machine Learning Projects by discussing the problem statement. Not only that, we will also be attaching the link to the dataset for you to practice. So, let us get straight into the discussion now.
Machine Learning Projects
1. House Price Prediction
How would it be if you could predict the appropriate price of a house? Wonderful, right? Yes, you can create a Machine Learning model which could predict the price of a house. The price of a house depends on various factors like the number of bedrooms, size of the house, location, etc.
It is a regression problem. Just type the values of the independent variables and you will get the right price of the house based on the factor values provided.
Remember to apply the feature engineering techniques required. You can even visualize the dataset for human comprehension. Using that, you will be able to explain to the end-users the correlation a location has on the price of a house.
In the dataset below, there are various features like Frontage Area, Location, etc. that you can use to predict the house price. For more Machine Learning Nanodegree review
2. Customer Churn Prediction
Customer Retention is a major challenge for financial institutes like Banks. The aim of the project is to classify if a customer is going to churn or not. It is extremely helpful for banks to identify and visualize which factors contribute to customer churn.
If banks could identify the customers who are going to churn and also identify the probable factors that may be leading them to churn, they can then create appropriate marketing and retention strategies to retain the customers. For instance, they could give the customers offers like a free credit card, low-interest loans, etc.
3. Heart Disease Prediction
Machine Learnings is finding its immense importance in the field of healthcare. It can predict various diseases like Heart Disease, Breast Cancer, etc.
Heart Disease is one such disease that can be predicted using Machine Learning. You need to provide the values of the factors contributing to heart disease like Blood Pressure, Chest Pain Type, Cholesterol, Sugar level, etc.
It is a binary classification problem.
The dataset contains 13 independent attributes. This dataset will enable you to practice feature engineering a lot. Also, you can explore different feature selection techniques to select the right features only to create the model. The dataset is highly imbalanced because many of the patients in this dataset did not develop heart disease. So, you can also explore techniques like Oversampling and Undersampling.
Are you a horror-movies lover or an action-film lover? You may be belonging to a specific group of these two. We often divide the people into different segments based on certain factors, which in this case is which genre of movies a person likes.
Customer Segmentation is an unsupervised learning problem. That means you don’t have a dependent variable.
Customer Segmentation is of prime importance for Markets and Companies. They want to divide the customers into different segments so that different marketing strategies can be applied to distinct segments to retain them. For example, the supermarket store might offer more discounts to the people who purchase from them rarely to attract them.
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Phishing is a kind of cybercrime where attackers pose as known or trusted entities and contact individuals through email, text, or telephone and ask them to share sensitive information. Users may also be prompted to enter credit card information or bank account details as well as other sensitive data. Once this information is collected, attackers may use it to access accounts, steal data and identities, and download malware onto the user’s computer.
To avoid this, the only solution is to identify if there is a threat of phishing or not based on certain factors. This is really important from the security point of view. It will be extremely helpful if we could determine if there is a possible threat of phishing.
6. TMDB Box Office Prediction
Everybody today loves watching films. So many major blockbuster hits are released every year, making hundreds of millions of dollars (sometimes even over 1 billion), that are exceedingly successful.
Can you predict a movie’s worldwide box office revenue? Through Machine Learning, It is possible.
It is a regression problem. The goal of this project is to analyze what makes particular movies successful, and others not so much, by a measure of worldwide box office revenue. It will be a boon for the film producers if they can get to understand what factors make a film successful.
In this dataset, you are provided with 7398 movies and a variety of metadata obtained from The Movie Database (TMDB). Movies are labeled with id. Data points include cast, crew, plot keywords, budget, posters, release dates, languages, production companies, and countries.
7. Human Activity Recognition with Smartphones
This is one of the best Machine Learning projects you can do. You can predict the activity performed by the person using the body posture values captured.
It is a multiclass classification problem. The objective is to classify activities into one of the six activities performed. The six activities are: Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying.
You can apply different Classification Algorithms like SVM, Naive Bayes, Random Forest, etc. to predict the output.
The dataset is available on UCI Machine Learning Repository.
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8. Census Income Prediction
Income Prediction is very useful for predicting the country’s economy and other various important measures. The goal of this machine learning project is to use the adult census income dataset to predict whether income exceeds 50K a year based on census data like education level, relationship, hours of work per week, and other attributes.
Based on the analysis, we can determine the income inequality gap between the rich and the poor. Also, we can analyze what factors contribute the most towards income inequality. Based on this, the governments can introduce appropriate policies to bridge the income gap and ensure good livelihood for all.
The dataset has over 32 thousand rows and 15 attributes. It is a great dataset for practicing how to deal with missing values and feature engineering.
9. NYC Taxi Trip Duration
This project is great to practice feature engineering. The aim of the project is to predict the total ride duration of taxi trips in New York City. It is a regression problem.
The dataset has variables that include start and end coordinates of a taxi trip, time, and the number of passengers. Variables like time and coordinates need to be pre-processed appropriately and converted into an understandable format. So, you get to practice dealing with dates also. This dataset also has some outliers that make prediction more complex, so you will need to handle this with feature engineering techniques.
You can explore various outlier detection and treatment techniques visually as well as statistically.
The project aims to forecast the inflow of migrants into various European Countries. By doing so, the government authorities can be proactive in preparing to meet their needs and advocate for the political will to provide safe passage into Europe.
Assistance is needed to be provided to the migrants. That’s why forecasting is of prime importance.
In the end, We would like to reiterate that projects are extremely important to gain mastery in any skill. It would help you in your overall learning process as well as for the interviews.
We discussed some of the best Machine Learning projects that will not just enable you to build the models but also strengthen your Feature Engineering skills.
Hope you would try these projects. Happy Learning!
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