Best 20 data science projects with source code

You have your sights set on an attractive career in data science. You know that you have the data science skills needed for the position. The issue is that you need more proof to support your data science skills. Anybody can claim to be a talented data scientist on their resume, but hiring managers will want evidence to support this claim. However, how can you show hiring managers that you’re worth their time by standing out like a perfect data scientist? Data science tasks are simple. Putting things into practice is the most efficient method!

Why Data Science Projects Are Important for a Successful Career in Data Science?

With IBM estimating 700,000 job openings by the end of 2020, data science is—and will always be—the hottest career choice, with demand for data professionals increasing steadily as the market expands. It takes an average of 60 days to fill an open data science position and 70 days to fill a senior data scientist position. CEOs and hiring managers at leading tech businesses aim for data scientists who can solve real-world challenges and connect their work to commercial value. Companies now employ people based on their ability to use data science rather than just academic knowledge. The greatest method to learn data science and develop a practical skill set is to begin working on data science projects.

Several years ago, most data science positions required a Master’s or Ph.D. in Mathematics or statistics. However, in recent years, things have changed.
The massive skills gap and the growth of data science careers have encouraged businesses to hire people who can add value to a company as quickly as feasible. Only by working with popular data science projects and completing several interesting projects will you learn how data architectures work in practice.
Also, as more organizations shift their machine learning solutions and data to the cloud, data scientists must master various associated tools and technologies to stay updated.

Unlocking the World of Data Science Projects: 20 Gems with Source Code

Data science is more than a word; it is an exciting field that allows us to extract useful information from the huge ocean of data. Whether you’re an experienced data scientist or a newbie, getting involved in practical projects is the greatest way to polish your abilities and remain current on the latest trends. This article will examine 20 interesting data science projects using source code, providing a hands-on approach to mastering this exciting study area.

1. Price Recommendation for Online Sellers

Many machine learning algorithms power a lot of e-commerce sites. These algorithms do everything from checking the quality of the products and managing the inventory to finding out who is buying what and suggesting products. E-commerce websites and apps are also trying to find ways to make it so that humans don’t have to make price ideas for sellers on their marketplace. This is another interesting business use case that they are trying to solve. That’s where machine learning for price prediction comes in.

You can use your data science skills on various datasets and in interesting projects to solve hard, real-world data science problems.
As part of this data science project, you will create a machine learning model to help online sellers find the best product prices. Different items with very small differences, like extra features or brand names, can have different prices depending on how much people want them. This is a difficult data science problem. It’s even harder to use price prediction models when there are millions of goods, which is the case with most eCommerce platforms.

2. Walmart Store’s Sales Forecasting

Big data and data science are used by e-commerce and retail to streamline business operations and make profitable decisions. Data science techniques are used to effectively handle a variety of functions, including inventory management, product recommendations to customers, and sales prediction. Walmart generated $482.13 billion in revenue in 2016 thanks to accurate estimates across its 11,500 locations through data science approaches. The name of this data science project makes it obvious that you will be working with a dataset of 143 weeks’ worth of sales transaction records from 45 Walmart shops and their 99 divisions.

Predicting future sales across many departments inside different Walmart shops is an interesting data science topic. This data science project’s most challenging aspect is predicting sales for the four biggest holidays: Labour Day, Christmas, Thanksgiving, and the Super Bowl. Walmart forecasts sales for these events to ensure enough product supply to satisfy demand. Markdown discounts, the consumer price index, whether the week was a holiday, weather, store size, store type, and unemployment rate are just a few details in the dataset.

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3. Personalized Medicine Recommending System

Recently, there has been much discussion among cancer researchers about how using genetic testing to treat diseases like cancer may revolutionize the field of cancer research. Clinical pathologists have made huge contributions that have helped bring this ideal revolution to fruition. The pathologist manually deduces the meaning of genetic alterations after initially sequencing a gene related to a malignant tumor. The pathologist must look for evidence in clinical literature to generate interpretations, which is time-consuming and laborious. But we can streamline this process by putting machine learning algorithms into practice. This project will be a good starting point for you to study the field of medicine and artificial intelligence integration.

With the data from the Memorial Sloan Kettering Cancer Centre (MSKCC) dataset, automate the classification of every genetic mutation found in the cancer tumor. The collection includes neutral mutations (passengers) and mutations classified as drivers of cancer growth. Reputable scientists and oncologists have manually marked the dataset.
Use the MSKCC dataset to create an automated system that can categorize genetic mutations in cancer tumors into classes of drivers and passengers.

4. Sentiment Analysis of Social Media Data

Use natural language processing (NLP) to assess the sentiment of messages on social media. By training models on labeled data, businesses, and marketers can gain useful insights by classifying tweets or Facebook comments as positive, bad, or neutral.

5. Loan Prediction Project Using Data Science

Banks mostly rely on loans to generate revenue, as a significant portion of their profits comes from the interest paid on these loans. However, approving a loan requires comprehensive validation and verification depending on several variables. Furthermore, despite complete verification, banks still determine if a borrower can return the loan without delay. The loan qualifying procedure is now automated in real-time by nearly all banks using machine learning. The process is based on several variables, including credit score, marital and employment status, gender, number of dependents, income, and expenses.

In this data science project, you will develop a predictive model to automate the selection of loan candidates. In this data science challenge, you must classify loan applicants based on their personal information to determine whether they can repay the loan. You will analyze exploratory data first, preprocess it, and test the created model. After completing this project, you will learn how to use machine learning to solve categorization challenges.

6. Credit Card Fraud Detection using Machine learning

This is an interesting project for data scientists who are ready to step outside of their comfort zone and tackle classification problems with a significant imbalance in the sizes of the target groups. The goal of credit card fraud detection is typically understood as a classification problem, wherein the transactions done on a certain credit card are classified as either fraudulent or valid. Because banks are hesitant to disclose their customers’ information owing to privacy concerns, there aren’t enough credit card transaction datasets accessible for practice.

To identify fraudulent credit card transactions from highly imbalanced and anonymous credit card transactional datasets, this data science project aims to assist data scientists in creating an intelligent credit card fraud detection model. The well-known Kaggle dataset, which includes credit card transactions conducted in September 2013 by European cardholders, was used to solve this data science challenge. Of the 284,807 transactions in this credit card transactional dataset, 492 (0.172%) were fraudulent. The positive class, or the number of frauds, only makes up 0.172% of all the credit card transactions in the sample, making it a very unbalanced dataset. The dataset contains 28 anonymized features produced by principal component analysis feature normalization. The amount in dollars and the time the transaction was made are the two other elements in the dataset that have not been anonymized. This will assist in determining the total cost of fraud.

7. Credit Analysis using Data Science

Several multinational banking institutions have started using artificial intelligence classification techniques for loan applications. They request that clients provide particulars regarding themselves.
Next, they apply machine learning algorithms to the gathered data to determine whether or not their clients can repay the loan for which they have used. One may also construct an undertaking utilizing the German Credit Dataset as a foundation.

To classify loan applications, utilize the German Credit Dataset. Dataset details relate to roughly one thousand loan applicants. In addition, we have twenty feature variables per applicant. Thirteen of these twenty attributes can accept discrete values, while three can accept continuous values. The objective is to extract crucial attributes from the dataset and employ those attributes in the classification process.

8. Climate Change Analysis and Predictions

Climate change seriously threatens our world, with long-term consequences for ecosystems, economies, and human well-being. To solve this global problem, meaningful insights from wide climate data analysis are urgently required. The task is first to analyze the current condition of the climate and identify trends and patterns; second, to use this knowledge to anticipate future climate scenarios and assess the environmental impact of human activities.

Contribute to environmental sustainability by analyzing climate data and predicting future changes. Data scientists can use machine learning models to measure the ecological impact of human activities and make well-informed decisions about reducing climate change.

9. Music Recommendation System

Dive into the realm of music recommendation systems, which assist users in finding new songs and musicians according to their listening tastes. Data scientists can make customized playlists and recommendations for music lovers by examining listening habits and user behavior.

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10. Market Basket Analysis in Python using Machine Learning

Whenever you enter a retail supermarket, you will see baby diapers and wipes, bread and butter, pizza base and cheese, alcohol, and chips all displayed together for sale. This is what market basket analysis is all about — examining the link among products bought together by customers. Market basket analysis is a varied use case in the retail industry that allows physical stores to cross-sell products and e-commerce sites to offer products to clients based on product connections. Apriori and FP growth are the most commonly utilized machine learning algorithms for association learning in market basket analysis.

To find hidden insights on enhancing product suggestions for clients, you will do a Market Basket Analysis in Python using the Apriori and FP Growth Algorithm based on association rules in this data science project. You will discover how to assess the association rules using a variety of measures, including Lift, Confidence, and Support.

11. Emotion Recognition in Facial Expressions

Developing algorithms that identify facial expressions will help you understand the complexities of human emotions. Emotion recognition has a wide range of practical uses, including detecting emotions in images or films for market research or applications related to mental health.

12. Plant Identification using Machine Learning

Image classification is a great way to use deep learning. The goal is to put all the pixels in an image into one of the predefined groups. Some of the most potential ways to connect computer vision and botanical taxonomy are plant image identification using deep learning and computer vision. You can start with this interesting data science project idea if you want to learn more about the amazing world of computer vision.

13. House Price Prediction using Machine Learning

This is not true if you believe that machine learning has divided the real estate business. The company has long used machine learning algorithms, with the website Zillow serving as a notable example. Zillow offers a tool called Zestimate, which uses public data to estimate the worth of a house. If you are interested, you should add this project to your list of data science projects.

14. Text Summarization and Document Understanding

Developing text summarization algorithms can simplify the work of extracting and understanding information. Text summarizing is an effective method for information management, whether used to condense large articles into summaries or remove significant ideas from research papers.

15. Customer Segmentation for Marketing Strategies

Divide clients into categories based on their behavior, demographics, or interests. Businesses can use clustering techniques such as k-means or hierarchical clustering to target certain client segments better.

16. Fake News Detection using Machine Learning

These days, fake news spreads very quickly on social media, chat apps, and other online resources. It is often made and sprinkled to trick or confuse people, and it can have very bad effects, ranging from changing public opinion to affecting politics and health. AI-based models make it easy to find this kind of news and mark it with a warning.

Through this project, you will learn how to use natural language processing (NLP) and deep learning models to make a system that can spot fake news. A sequence problem is something you will learn about in NLP. You will use models like RNN, GRU, and LSTM to solve it. You will also learn how to use cleaning and preparing text methods, such as removing stopwords, stemming, tokenization, padding, and more. You will also have the chance to look into text vectorization and word embedding models.

17. Stock Market Prediction using Machine Learning

“How long should we hold on to a stock for?” is the question most stock buyers love to ask. Many investors want to know how to avoid being too scared or greedy. And not all of them have Warren Buffett to help them along the way. You should stop looking for him. Instead, it would help if you used AI tools like Machine Learning to make your stock market forecast. You should add this to your list of Data Science Projects because it’s so easy to do.

Use machine learning techniques on the EuroStockMarket Dataset to make a system for predicting the stock market. This dataset has the closing prices of the UK FTSE, Germany’s DAX (Ibis), Switzerland’s SMI, France’s CAC, and Germany’s DAX. It includes prices for all working days.

The goal of the Stock Market Prediction Data Science Project is to use the given information to guess what the stock price will be.

19. Wine Quality Prediction

Most of us want to have a fancy dinner with our loved ones on the weekends. Adults like to finish a gourmet dinner with a traditional red wine to go with their Italian cuisine, even though children only consider pasta fancy. However, some of us need help deciding which wine bottle is ideal when shopping. Only a few people think that the longer something is fermented, the better it will taste. Few argue that comparatively sweeter wines are of higher quality. You can create your own wine Quality Predictor to find the exact answer.

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20. Health Monitoring and Disease Prediction

Create models for disease prediction and health monitoring to take advantage of the potential of data science to enhance healthcare outcomes. Data-driven methods can completely change how healthcare is provided, from anticipating the beginning of chronic illnesses to remotely monitoring vital signs.

To sum up, data science presents many possibilities for research and creativity. You can boost your career by taking on these 20 data science projects with source code, giving you practical experience and abilities. A data science project is waiting for you to get involved and change the world, regardless of your interests in marketing, technology, healthcare, finance, or any other industry.

How difficult are these data science projects for beginners?

These projects vary in how hard they are, but many have step-by-step instructions and other materials to help people who are just starting. A great way to gain confidence and skills is to start with easier projects and work up to more difficult ones.

Do I need prior programming experience to work on these projects?

You should know how to use programming languages like Python, but many projects have tools and tutorials to help you get started. Even people who have never done these projects can finish and learn from the process if they work hard.

Can I modify these projects to suit my specific interests or goals?

Of course! One great thing about working on data science projects is that you can change and adapt them to your needs. These projects give you a solid base for experimenting and being creative, whether you want to learn more about a certain business, try different algorithms, or solve certain problems.