Data science projects for Beginners-Apply your data science skills to interesting data science project ideas and solve real-world data science problems.
Last Updated: 11 Jul 2024 | BY ProjectPro You've got your eyes on a rewarding job in data science with your name written all over the data scientist job title. You know you have the data science skills required for the job. The problem is that you need something to prove you have a versatile data science skill set. Anyone can mention on their data science resume that they're a skilled data scientist – hiring managers will want you to back it up with some solid examples; otherwise, you will be ready to get dropped like a bad AOL connection. But how can you stand out like a bug-free production-quality data science code and show hiring managers that you're worth your salt? Easy – Data science projects. The most effective way to do it is to do it! Well, we believe in this. This blog is an excellent medium for beginners to get their hands dirty on data science while working on some cool and exciting data science project ideas. We encourage you to have fun by exploring our list of diverse data science and machine learning projects.
For those of you already working in the data science industry or looking to break into the world of data science with your first data science job, the number of processes, machine learning algorithms, knowledge extraction systems, data science tools, and technologies that you are expected to know can be overwhelming. Python, R, NLTK, TensorFlow, Keras, Tableau, Jupyter, iPython Notebook, Matplotlib. The list goes on … But don’t fear! We have collated 20+ data science projects for beginners to get you started and point you to the appropriate resources on the web for further understanding.
Here is a list of easy data science projects that you can work on as a beginner in the evolving field of data science.
1) Churn Prediction using Machine Learning
2) Sentiment Analysis of Product Reviews
3) Price Recommendation using Machine Learning
5) Sales Forecasting
6) Building a Recommender System
7) Employee Access-Challenge as a Classification Problem
8) Survival Prediction using Machine Learning
9) Personalized Medicine Recommending System
10) Image Masking
11) Loan Default Prediction
12) Fraud Detection as a Classification Problem
13) Macro-economic Trends Prediction
14) Credit Analysis
15) Model Insurance Claim Severity
16) Build a Chatbot from Scratch in Python using NLTK
17) Market Basket Analysis using Apriori
18) Build a Resume Parser using NLP -Spacy
19) Build an Image Classifier Using Tensorflow
20) House Price Prediction using Machine Learning
21) Recommendation System for Retail Stores
22) Fake News Detection
23) Human Activity Recognition
As a beginner in data science, we recommend that you first experiment with mini projects for data science students, as highlighted in this section.
Predicting churn for a video streaming service is crucial for revenue preservation, cost reduction, and enhancing customer experience. It enables targeted retention strategies, reduces customer acquisition costs, and leads to data-driven decision-making. By understanding factors leading to churn, such as content preferences and service quality, the company can personalize offerings, improve satisfaction, and maintain a competitive edge in the market. The good news is that all these factors can be quantified with different layers of data about billing history, subscription plans, cost of content, network/bandwidth utilization, and more to get a 360-degree view of the customer. This 360-degree view of customer data can be leveraged using predictive modeling techniques to identify patterns and various trends that influence customer satisfaction and help reduce churn.
Considering that customer churn is expensive and inevitable, leveraging analytics to understand the factors influencing customer attrition, identifying customers most likely to churn, and offering them discounts can be a great way to reduce it. In this data science project, you will build a decision tree machine learning model to understand the correlation between the different variables in the dataset and customer churn. This churn prediction machine learning project will tweak the problem of unsatisfied customers and make the revenue flow for the streaming company.
Product reviews from users are the key for businesses to make strategic decisions as they give an in-depth understanding of what the users want for a better experience. Today, almost all companies have reviews and rating sections on their website to understand if a user’s experience has been positive, negative, or neutral. With an overload of puzzling reviews and feedback on the product, it is impossible to read each review manually. Also, the feedback often has many shorthand words and spelling mistakes that could be difficult to decipher. That is where sentiment analysis comes to the rescue.
In this data science project, you will use natural language processing techniques to preprocess and extract relevant features from the reviews and rating dataset. You will then use a semi-supervised learning methodology to apply the pairwise ranking approach to rank reviews and further segregate them to perform sentiment analysis. The developed model will help businesses maximize user satisfaction efficiently by prioritizing product updates that will likely have the most positive impact.
E-commerce platforms today are extensively driven by machine learning algorithms, from quality checking and inventory management to sales demographics and product recommendations; all use machine learning. One more exciting business use case that e-commerce apps and websites are trying to solve is to eliminate human interference in providing price suggestions to the sellers on their marketplace to speed up the efficiency of the shopping website or app. That’s when price recommendation using machine learning comes into play.
In this data science project, you will build a machine learning model that will automatically suggest the correct product prices to online sellers as accurately as possible. It is a challenging data science problem statement since similar products with very slight differences, like additional specifications, different brand names, or the demand for the product, can have different product prices. Price prediction modeling becomes even more challenging when there are lakhs of products, which is the case with most e-commerce platforms.
In this section, you will explore simple data science projects for beginners to practice using the dataset available on Kaggle.
Ecommerce & Retail use big data and data science to optimize business processes and for profitable decision-making. Various tasks, like predicting sales, offering product recommendations to customers, inventory management, etc., are elegantly managed using data science techniques. Walmart has used data science techniques to make precise forecasts across its 11,500 revenue, generating $482.13 billion in 2016. As it is clear from the name of this mini project for data science, you will work on the Walmart store dataset that consists of 143 weeks of transaction records of sales across 45 Walmart stores and their 99 departments.
Here is an exciting problem statement in data science that involves forecasting future sales across various departments within different Walmart outlets. The challenging aspect of this data science project is forecasting sales on four major holidays – Labor Day, Christmas, Thanksgiving, and the Super Bowl. The selected holiday markdown events are when Walmart makes the highest sales, and by forecasting sales for these events, they want to ensure sufficient product supply to meet the demand. The dataset contains markdown discounts, consumer price index, whether the week was a holiday, temperature, store size, store type, and unemployment rate. The project aims to forecast Walmart store sales across various departments using the historical Walmart dataset and predict which departments are affected by the holiday markdown events and the extent of the impact. To make predictions, you will use ML models like the Linear Regression Model, Random Forest Regression Model, K Neighbors Regression Model, XGBoost Regression Model, and a Custom Deep Learning Neural Network.
Everybody wants their products to be personalized and behave as they wish. A recommender system aims to model a product's preference for a particular user. This data science project aims to study the Expedia Online Hotel Booking System by recommending hotels to users based on their preferences. The Expedia dataset was made available as a data science challenge on Kaggle to contextualize customer data and predict the probability of a customer likely to stay at 100 different hotel groups.
The Expedia dataset consists of 37,670,293 entries in the training set and 2,528,243 in the test set. Expedia Hotel Recommendations dataset has data from 2013 to 2014 as the training set and the data for 2015 as the test set. The dataset contains details about check-in and check-out dates, user location, destination details, origin-destination distance, and the actual bookings made. Also, it has 149 latent features extracted from the hotel reviews provided by travelers dependent on hotel services like proximity to tourist attractions, cleanliness, laundry service, etc. All the user IDs that are present in the test set are present in the training set.
This project solution aims to predict a user's likelihood to stay at 100 different hotel groups, rank the predictions, and return the top 5 most likely hotel clusters for each user's search query in the test set. The problem falls under the category of multi-class classification problems, which you will solve by implementing Naive Bayes, Logistic Regression, and KNN algorithms over the dataset in Python.
Determining various resource access privileges for employees is a popular real-world data science challenge for giant companies like Google and Amazon. For companies like Amazon, various human resource administrators had done this earlier because of their highly complicated employee and resource situations. Amazon was interested in automating the process of providing its employees with access to various computer resources to save money and time. So, they announced a challenge on Kaggle: to build an employee access control system that automatically approves or rejects employee resource applications.
The dataset for this data science project for beginners consists of historical data of 2010 -2011 recorded by human resource administrators at Amazon Inc. The training set consists of 32769 samples and the test set consists of 58922 samples. Every dataset sample has eight features that indicate a different role or group of an Amazon employee.
Working on this project solution will teach you to work with a highly imbalanced dataset. You will learn to use the random forest model in Python to determine employees' resource access privileges automatically.
Here, we have one of the popular beginner data science projects in the global community for data science beginners because the solution to this problem provides a clear understanding of what a typical data science project consists of.
The data science problem statement for this project involves predicting the fate of passengers aboard the RMS Titanic, which famously sank in the Atlantic Ocean after colliding with an iceberg during its voyage from the UK to New York. The aim of this data science project is to predict which passengers would have survived on the Titanic based on their characteristics, such as age, sex, class of ticket, etc.
Work on this project to learn about Python's various data types, control structures, and looping concepts. Explore how Data science libraries in Python, like NumPy, Pandas, Scikit-learn, etc., are used to solve a supervised machine learning problem.
The recent talk of the town among Cancer Researchers is how treating diseases like Cancer using Genetic Testing will revolutionize the universe of Cancer Research. This dreamy revolution has been partially realized because of the significant efforts of clinical pathologists. The pathologist first sequences a cancer tumor gene and then manually interprets the genetic mutation. It is quite a tedious process and takes a lot of time as the pathologist has to look for evidence in clinical literature to derive interpretations. However, this process can be smoother if we implement machine learning algorithms. This project will be a good start if you want to explore a field that integrates Medicine and Artificial Intelligence.
The goal is to automate classifying every single genetic mutation of the cancer tumor using the dataset prepared by Memorial Sloan Kettering Cancer Center (MSKCC). The dataset contains mutations labeled as tumor growth (drivers) and neutral mutations (passengers). World-renowned researchers and oncologists have manually annotated the dataset.
You will learn how to design an automated system that can classify genetic mutations in cancer tumors into classes of drivers and passengers using the MSKCC dataset. You will be understanding the implementation of Natural Language Processing techniques. This project will guide you through merging two Python dataframes, utilizing the word_cloud library, understanding the differences between Summing and Lemmatization, implementing the Tf_Idf Vectorizer, and applying the Long-Short-Term Memory (LSTM) Deep Learning model to the given dataset.
Often, we come across images from which we wish to remove background and utilize them for specific purposes. Carvana, an online startup, has attempted to build an automated photo studio that clicks 16 photographs of each vehicle in its inventory. Cavana captures these photographs with bright reflections in high resolution. However, the cars in the background sometimes make it difficult for their customers to look at their choice vehicle closely. Thus, an automated tool that can remove background noise from the captured images and only highlight the image's subject would work like magic for the startup and save tons of hours for their photo editors. You can also implement an image masking system that automatically removes background noise.
This data science project solution will use the Carvana Dataset and implement a neural network algorithm to design an Image Masking system that removes photo studio backgrounds. This implementation, built using the Tensorflow and Keras Framework, will make it easy to prepare images containing backgrounds that bring the car features into the limelight. The project will use data augmentation techniques to improve the model's performance and explore methods to change various image features such as brightness, contrast, etc.
In this section, you will find a list of project ideas for beginners in data science from the finance industry
Loans are the core revenue generators for banks as a significant part of the profit for banks comes directly from the interest of these loans. However, the loan approval process is intensive, with much validation and verification based on multiple factors. And even after so much verification, banks still need to be assured that a person can repay the loan without difficulties. Today, almost all banks use machine learning to automate the loan eligibility process in real-time based on factors like Credit Score, Marital and Job Status, Gender, Existing Loans, Total Number of Dependents, Income, and Expenses, among others.
It is an exciting data science project in the financial domain.You will build a predictive model to automate targeting suitable loan applicants. This data science problem is a classification problem where you use information about a loan applicant to predict if they can repay the loan. You will begin with exploratory data analysis, then data preprocessing, and finally, testing the developed model. After completing this project, you will develop a solid understanding of solving classification problems using machine learning.
Here, we have an exciting data science problem for data scientists who want to get out of their comfort zone by tackling classification problems caused by a significant imbalance in the size of the target groups. Credit card fraud detection is usually a classification problem that classifies transactions made on a particular credit card as fraudulent or legitimate. More credit card transaction datasets must be available for practice, as banks do not want to reveal their customer data due to privacy concerns.
This data science project aims to help data scientists develop an intelligent credit card fraud detection model for identifying fraudulent credit card transactions from highly imbalanced and anonymous credit card transactional datasets. To solve this project related to data science, the popular Kaggle dataset contains credit card transactions made in September 2013 by European cardholders. This credit card transactional dataset consists of 284,807 transactions, of which 492 (0.172%) transactions were fraudulent. It is a highly unbalanced dataset as the positive class, i.e., the number of frauds accounts only for 0.172% of all the credit card transactions in the dataset. There are 28 anonymized features in the dataset that are obtained by feature normalization using principal component analysis. Two additional features in the dataset have not been anonymized – the time when the transaction was made and the amount in dollars. It will help detect the overall cost of fraud.
The data science problem statement for this project aims to identify the number of fraudulent transactions in the dataset and predict the accuracy of the model developed. You can implement the solution by working on this imbalanced dataset and building a predictive model using ML algorithms like Random Forests, K-Nearest Neighbour, and Logistic Regression.
We often hear from the news channels that XYZ country will be one of the biggest economies in the world in the year 2030. If you have ever wondered the basis for such statements, allow me to help you. These news channels rely on statisticians-cum-Data Scientists to come up with such predictions. These data scientists analyze several financial datasets of various countries and then submit their conclusions which then make the headlines. If you are interested in a project that revolves around this area, you are on the right page.
The aim of this project solution is to design a macro-economic trends predictor using Machine learning algorithms, including linear regression, Ridge Regression, XGBoost, and elasticnet models. After implementing the models, you will deduce which model works best by plotting relevant graphs.
Many multinational companies of the Banking sector have now started relying on Artificial intelligence techniques that allow them to classify loan applications. They request their customers to submit specific details about themselves.
They then utilize these details and implement machine learning algorithms on the collected data to understand the ability of their customers to repay the loan they have applied for. You can also attempt to build a project around this using the German Credit Dataset.
The data science problem statement for this project is to use the German Credit Dataset to classify loan applications. The dataset contains information about about 1,000 loan applicants. For each applicant, we have 20 feature variables. Out of these 20 attributes, three can take continuous values, and the remaining seventeen can take discrete values. This problem will be solved by extracting essential features from the dataset and using those features for classification.
You will learn to implement the Logistic Regression algorithm and improve its performance using the Random Forest algorithm. You will also learn to train a Neural Network Algorithm and explore commonly used metrics in Machine Learning to analyze which algorithm is better.
Nobody wants to drain their time and energy on filing insurance claims and dealing with all the paperwork with an insurance broker or an agent. To make the insurance claims process hassle-free, insurance companies across the globe are leveraging data science and machine learning to make this claims service process easier. This beginner-level data science project is about how insurance companies are predictive machine learning models to enhance customer service and make the claims service process smoother and faster.
Whenever a person files an insurance claim, an insurance agent reviews all the paperwork thoroughly and then decides on the claim amount to be sanctioned. This entire paperwork process to predict the cost and severity of the claim is time-taking. In this project, you will build a machine learning model to predict the claim severity based on the input data. This project will make use of the Allstate Claims dataset that consists of 116 categorical variables and 14 continuous features, with over 300,000 rows of masked and anonymous data where each row represents an insurance claim.
In this section, you will find data science projects for beginners in Python with source code for most project ideas.
Do you remember the last time you spoke to a customer service associate on call or via chat for an incorrect item delivered to you from Amazon, Flipkart, or Walmart? You would have talked with a chatbot instead of a customer service agent. Gartner estimates that 85% of customer interactions will be handled by chatbots by 2022. So what exactly is a chatbot? How can you build an intelligent chatbot using Python?
A chatbot is an AI-based digital assistant that can understand human capabilities and simulate human conversations in natural language to give prompt answers to their questions just like a real human would. Chatbots help businesses increase their operational efficiency by automating customer requests.
The most important task of a chatbot is to analyze and understand the intent of a customer request to extract relevant entities. Based on the analysis, the bot then responds appropriately to the user. Natural language processing plays a vital role in text analytics through chatbots, making the interaction between computers and humans feel like real conversations. Every chatbot works by adopting the following three classification methods-
In this data science project, you will use a leading and powerful Python library, NLTK (Natural Language Toolkit), to work with text data. You will use preprocessing techniques like Tokenization and Lemmatization to preprocess the textual data.
Whenever you visit a retail supermarket, you will find baby diapers and wipes, bread and butter, pizza base and cheese, beer, and chips positioned together in the store for sale. That is what market basket analysis is all about – analyzing the association among products bought together by customers. Market basket analysis is a versatile use case in the retail industry that helps cross-sell products in a physical outlet and also helps e-commerce businesses recommend products to customers based on product associations. Apriori and FP growth are the most popular machine learning algorithms used for association learning to perform market basket analysis.
In this beginner-level data science project, you will perform Market Basket Analysis in Python using Apriori and FP Growth Algorithms based on association rules to discover hidden insights on improving product recommendations for customers. You will learn to apply various metrics like Support, Lift, and Confident to evaluate the association rules.
Gone are the days when recruiters manually screened resumes for a long time. Thanks to resume parsers, sifting through thousands of candidates' resumes for a job is now easy. Resume parsers use machine learning technology to help recruiters search thousands of resumes intelligently to screen the right candidate for a job interview.
A resume parser or a CV parser is a program that analyses and extracts CV/ Resume data according to the job description and returns machine-readable output that is suitable for storage, manipulation, and reporting by a computer. A resume parser stores the extracted information for each resume with a unique entry, thereby helping recruiters get a list of relevant candidates for a specific search of keywords and phrases (skills). Resume parsers help recruiters set a specific criterion for a job, and candidate resumes that do not match the set criteria are filtered out automatically.
In this data science project, you will build an NLP algorithm that parses a resume and looks for the words (skills) mentioned in the job description. You will use the Phrase Matcher feature of the NLP library Spacy, which does "word/phrase" matching for resume documents. The resume parser then counts the occurrence of words (skills) under various categories for each resume, helping recruiters screen ideal candidates for a job.
Image classification is a fantastic application of deep learning. The objective is to classify all the pixels of an image into one of the defined classes.
Plant image identification using deep learning is one of the most promising solutions to bridging the gap between computer vision and botanical taxonomy. If you want to take your first step into the amazing world of computer vision, this is an exciting data science project idea to start.
This section boasts of a list of data science project topics that are more challenging than the ones we have discussed already, yet, can easily be labeled as fun data science projects to understand the practical applications of data science.
If you think real estate is one such industry that has been alienated by Machine Learning, then we'd like to inform you that it is not the case. The industry has been using Machine learning algorithms for a long time, and a famous example is the website Zillow. Zillow has a tool called Zestimate that estimates the price of a house based on public data. If you're a beginner, it'd be a good idea to include this project in your list of data science projects.
In this data science project, the task is to implement a regression machine-learning algorithm for predicting the price of a house using the Zillow Dataset. The dataset contains about 60 features and two files, 'train_2016' and 'properties_2016'. The files are linked through each other via a feature called 'parcelid'. This project aims to implement a machine learning model that can predict the best future sale predictions of houses.
You will learn how to clean the dataset and techniques for replacing missing data values. You will also learn how to use exciting data visualization libraries in Python: Matplotlib, seaborn. Using statistical methods, you will explore the dataset and understand what features are relevant for training a machine learning algorithm. Additionally, you will be guided on training different machine learning algorithms for regression problems.
In case you have tried shopping online, you must have seen the website trying to recommend you a few products. Have you ever wondered how such websites develop products you are highly likely to display interest in? Well, that's because machine learning-based algorithms are running in the background, and this project is all about it.
This project aims to use the retail store dataset to build an efficient recommendation system for them and perform Market Basket Analysis. This project will help you draw customer insights by performing exploratory data analysis of the given dataset. You will learn about date-time and free items analysis, evaluating deals of the day, and tending items selection by analyzing the dataset at the item level. Additionally, you will explore the Apriori algorithm and association rules.
Fake news spreads rapidly through social media, messaging apps, and other digital platforms. It is often created and circulated with the intent of misleading or manipulating people, and can have serious consequences, from influencing public opinion to impacting political outcomes and public health. With AI-based tools, these kind of news can be easily detected and used to tag them with a disclaimer.
The project will guide you on how to use NLP and deep learning models to build a system that can detect fake news. You will learn how to work on a sequence problem in NLP and use models like RNN, GRU, and LSTM to solve such problems. You will also learn how to implement text cleaning and preprocessing methods like stopword removal, stemming, tokenization, padding, etc. Besides that, you will also get to explore text vectorization and word embedding models.
time. With exciting watches being designed by multiple international brands, people are now gradually switching to smartwatches. Smartwatches are cool watches of the 21st century that have made their way into almost every household. The prime reason for this is the attractive features that they offer. They can do nearly anything from heart-rate monitoring and ECG monitoring to workout-tracking. If you have used one such watch, you can recall that it often tells you how well you slept. So, how come a device that never sleeps can guide you about your sleep? To find an answer, you can do a simple data science project that associates a dataset of a few people's daily activities with the data collected by various sensors attached to those people.
In this data science project, you are expected to use machine-learning algorithms to assign the Human Activity Recognition Dataset features a class out of these six: WALKING, WALKING_UPSTAIRS WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING. The Human Activity Recognition Data Science Project aims to build a system that can classify human activities by considering specific features.
In this project, you will learn to implement exploratory data analysis techniques to gain insights into the dataset. You'll explore various data visualization libraries to craft insightful graphs that visualize trends effectively. Understanding Principal Component Analysis will help you shortlist relevant features for analysis. Before applying algorithms, you'll clean the dataset thoroughly to ensure data integrity. For the classification problem, you will experiment with machine learning algorithms like Logistic Regression, SVMs, Random Forest, and Neural Networks. You'll select the best model based on statistical metrics evaluation, ensuring optimal performance.
Before we put an end to this blog, we have a few Data Science Learning tips for you from Mohammed Sohaib, fromer Data Scientist at Pianalytix.
So, there you have some interesting data science project ideas to start working your way into data science. No matter whichever data science project you choose to begin, you will open up countless possibilities for developing your data science skills. Reading data science books and tutorials is definitely a great way of learning data science, but there's no substitution for actually building end-to-end solutions for challenging data science problems. Working on diverse, exciting data science projects is the perfect way to improve your data science skills and progress towards mastering them. Your hiring manager will be more impressed with your data science and machine learning projects on GitHub or on your data science portfolio than a list of books that you've read.
ProjectPro offers data science projects in python with source code that have a taste of diverse data science problems from different business domains. Each of these data science projects is designed to develop knowledge of the most popular data science tools and in-demand data science skills that employers are looking for. Professionals build end-to-end solutions for real-world data science problems and work accordingly by modeling the solutions as per their needs. Some of these data science projects are in Python and some in R. Some of these projects on data science are simple and some hard. However, these data science projects are great for resumes, especially before important whiteboard data science interviews. Nobody wants to be a starving data scientist anymore and the best way to learn data science is to do data science. Look for as many data science projects online as you can get involved in working with. Each data science project you work on will become a building block towards mastering data science leading to bigger and better data scientist job opportunities. The world needs better Data Scientists- This is the best time to learn data science by working on interesting data science projects.
Access 200+ solved data science and machine learning projects designed to provide data science enthusiasts with experiential learning experiences. Join the Data Science Game by working on some cool and exciting Data Science Projects.
You can find data for your projects on Google Dataset Search, UCI Machine Learning Repository, Kaggle, Github, Data.gov, and other major dataset search engines and paid data repositories.
To select the appropriate machine learning model for a given problem, first, the problem and the type of data available should be clearly defined. Then, based on the problem type and data characteristics, various factors such as the size of the dataset, the complexity of the model, the interpretability of the model, and the expected accuracy should be considered. The selection process usually involves experimenting with multiple models and selecting the one that provides the best results.
To avoid overfitting or underfitting in machine learning models, techniques such as regularization, early stopping, data augmentation, and hyperparameter tuning can be used. Regularization reduces model complexity, early stopping prevents overfitting, data augmentation increases the training dataset size, and hyperparameter tuning adjusts model performance.
