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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.

4. Customer Segmentation

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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5. Phishing Detection

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.

10. Migration Prediction

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.

Conclusion

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!

Let us know through your comments if it was helpful for you to kickstart your journey in Best data science Online Courses

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Top 13 Python Libraries Every Data science Aspirant Must know!

Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set, there’s no doubt about it. It has consistently ranked top in global data science surveys and its widespread popularity only keeps on increasing!

But what makes Python so special for data scientists?

Just like our human body consists of multiple organs for multiple tasks and a heart to keep them running, similarly, the core Python provides us with the easy easy-to-code, object-oriented, high-level language (the heart). We have different libraries for each type of job like Math, Data Mining, Data Exploration, and visualization(the organs). learn with these resources Best Python Books

It is of utmost importance that we master each and every library, these are the core libraries and these won’t be changed overnight. The AI and ML BlackBelt+ program help you master these 13 libraries along with many more.

That’s not all, you’ll get personalized mentorship sessions in which your expert mentor will customize the learning path according to your career needs.

Let us learn about the Top 13 Python libraries for data science that you must master!

Before starting out, I have a bonus resource for you! Python is a diverse language and it is hard to remember each and every line of syntax so here’s the link to the Python cheatsheet to help you out-

Math

NumPy

NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. NumPy provides support for large multidimensional array objects and various tools to work with them.

Various other libraries which we are going to discuss further like Pandas, Matplotlib and Scikit-learn are built on top of this amazing library! I have just the right resource for you to get started with NumPy –

SciPy

SciPy (Scientific Python) is the go-to library when it comes to scientific computing used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab which is a paid tool.

SciPy as the Documentation says is – “provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.” It is built upon the NumPy library. Learn new skills with Best Python Programming Books

Data Mining

BeautifulSoup

BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents.

BeautifulSoup automatically detects encodings and gracefully handles HTML documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the webpages. In this article, we will learn how to build web scrapers using Beautiful Soup in detail.

Scrapy

Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.

Data Exploration and Visualization

Pandas

From Data Exploration to visualization to analysis – Pandas is the almighty library you must master!

Pandas is an open-source package. It helps you to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data. Learn advanced concepts with Best book to learn python

Matplotlib

Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Every other library is built upon this library.

Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors, themes, palettes, and other options to customize and personalize our plots. matplotlib is useful whether you’re performing data exploration for a machine learning project or building a report for stakeholders, it is surely the handiest library!

Plotly

Plotly is a free and open-source data visualization library. I personally love this library because of its high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts.

It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework. So if you are looking to explore data or simply wanting to impress your stakeholders, plotly is the way to go!

Seaborn

Seaborn is a free and open-source data visualization library based on Matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.

Seaborn provides easy functions that help you focus on the plot and now how to draw it. Seaborn is an essential library you must master. 

Machine Learning

Scikit Learn

Sklearn is the Swiss Army Knife of data science libraries. It is an indispensable tool in your data science armory that will carve a path through seemingly unassailable hurdles. In simple words, it is used for making machine learning models.

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Sklearn is a compulsory Python library you need to master. Analytics Vidhya offers a free course on it. You can check out the resources here 

PyCaret

Tired of writing endless lines of code to build your machine learning model? PyCaret is the way to go!

PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It helps you save tons of time by being a low-code library.

It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models. Here’s an excellent resource for you to learn PyCaret from scratch  Learn basick skills with  best python book for beginners

TensorFlow

Over the years, TensorFlow, developed by the Google Brain team has gained traction and become the cutting edge library when it comes to machine learning and deep learning. TensorFlow had its first public release back in 2015. At the time, the evolving deep learning landscape for developers & researchers was occupied by Caffe and Theano. In a short time, TensorFlow emerged as the most popular library for deep learning.

TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications.

Keras

Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. According to Keras – “Being able to go from idea to result as fast as possible is key to doing good research.”

Keras is preferred over TensorFlow by many, due to its much better “user experience”, Keras was developed in Python and hence the ease of understanding by Python developers. It is simple to use and yet a very powerful library.

PyTorch

Many data science enthusiasts hail Pytorch as the best deep learning framework (that’s a debate for later on). It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive.

PyTorch is a Python-based library that provides maximum flexibility and speed. Some of the features of Pytorch are as follows –

Production Ready

Distributed Training

Robust Ecosystem

Cloud support

End Notes

Python is a powerful yet simple language for all of your machine learning tasks.

In this article, we discussed 13 libraries that will help you achieve your data science goals like maths, data mining, data exploration, and visualization, machine learning and Advanced python Book