Data Science

Top 10+ Data Science Projects With Source Code for 2024

Published: 25th September, 2023

Harshini Bhat

Data Science Consultant at almaBetter

Dive into hands-on data science projects with source code from basic to advanced level. Elevate your skills with practical examples and real-world applications.

After the learning process, comes the process of practice and application. Applying the things you have learned so far is really important. It answers to the most important question

‘What can be done with the things we learned’. We can gain experience and confidence from each  project we create that can also help us crack Data Science Jobs with these best data science projects.

In the vast landscape of data science, theory alone can be like a compass without a map. However, projects are cartography that transforms theoretical knowledge into real-world mastery. The real-world data science projects offer the crucial bridge between learning and application, enabling you to tackle authentic data challenges.

In this article, we will discuss top trending data science mini projects as well as end-to-end projects for beginners, intermediate and advanced levels.

"Experience is the best teacher."

Data Science Project Ideas

Data science has surged in popularity due to its transformative potential across diverse industries. According to reports, its demand is particularly pronounced in fields like healthcare, finance, e-commerce, and technology. In healthcare, data science drives innovations in patient care, diagnosis, and drug discovery. Financial institutions rely on data science for risk assessment, fraud detection, and algorithmic trading. E-commerce thrives on data-driven marketing, personalization, and supply chain optimization. In the technology sector, data science underpins everything from recommendation systems to AI-driven product development. The versatility and power of data science make it a sought-after skill, with its influence extending into nearly every facet of the modern economy.

Data science projects serve as the proving ground where theory meets reality, offering invaluable hands-on experience in this dynamic field. These projects are the cornerstone of a data scientist's journey, where abstract concepts find concrete application. They allow you to harness your knowledge to solve real-world problems, from predicting customer preferences to automating decision-making processes. By diving into these projects, you not only build a robust portfolio but also develop a deeper understanding of data analysis, Machine Learning algorithms, and data visualization.

In this article, we'll delve into a curated collection of data science projects complete with source code, providing a treasure trove of learning resources and practical insights to propel your data science aspirations.

Data Science Projects for Beginners

For those venturing into the exciting world of data science, the journey often begins with foundational projects. These projects on data science serve as a gentle entry point, allowing newcomers to apply their burgeoning skills to real-world scenarios. In this section, we'll explore a curated selection of beginner-friendly projects that provide a solid stepping stone to the captivating universe of data science. These projects not only help build essential skills but also instill the confidence needed to tackle more complex data challenges in the future. So, let's embark on this data-driven journey tailored for beginners and witness the magic of turning data into insights.

COVID-19 Vaccination Progress

As the world grapples with the far-reaching impact of the COVID-19 pandemic, understanding the dynamics of vaccination progress has become paramount. This project can be served as a data science mini project. The 'COVID-19 Vaccination Progress' analysis project embarks on a comprehensive exploration of this vital global effort. Within this dataset, which encompasses an array of critical metrics, we unveil the intricate details of vaccination campaigns worldwide. From deciphering the various vaccination schemes adopted by different countries to examining the daily vaccination rates and their per-million population equivalents, we aim to provide a holistic view of the ongoing vaccination journey. Furthermore, we delve into the total number of vaccinations administered, the percentage of vaccinated populations, and the complete vaccination landscape. Through the lens of insightful visualizations and meticulous analysis, we strive to reveal the ever-evolving narrative of global vaccination progress and its pivotal role in our collective battle against the pandemic.

This project is one of the data science projects in python that introduces the significance of data visualization using Plotly, a popular Python library. You can learn how to create insightful visualizations that communicate complex information effectively. You'll explore techniques for plotting time-series data, comparing trends, and visualizing variation across countries. You can also gain insights into descriptive statistics, aggregating and summarizing data to answer critical questions. In this project, we are going to focus on understanding vaccination schemes, daily vaccination rates, and the percentage of vaccinated populations.

Source Code - COVID-19 Vaccination Progress

Recommender System Using Amazon Reviews

  • Language: Python
  • Dataset: CSV file

In the digital age, where information overload is the norm, recommender systems have emerged as indispensable tools to help users discover products tailored to their preferences. The 'Recommender System Using Amazon Reviews' project delves into the intricate world of recommendation systems, using a rich dataset encompassing user IDs, product IDs, ratings, and timestamps. E-commerce giants like Amazon and Flipkart rely heavily on recommendation systems to enhance user experience and boost sales. These systems are designed to bridge the gap between users and products, offering personalized suggestions that drive engagement and revenue.

We harness the power of Python in this data science mini projects in python and its specialized libraries. Our toolkit includes numpy and pandas for data manipulation, while visualization is brought to life with matplotlib and seaborn. We tap into the mathematical magic of scipy for sparse matrix operations and scikit-learn for nearest neighbor algorithms.

In this project, we explore the foundations of recommendation systems, their types, and their pivotal role in modern digital platforms.

Source Code - Recommender System Using Amazon Reviews

Price Prediction

  • Language: Python
  • Dataset: CSV file

In today's e-commerce landscape, understanding price trends is pivotal for businesses. Price prediction can help e-commerce companies optimize pricing strategies, enhance competitiveness, and drive sales. This project harnesses the potential of recurrent neural networks (RNNs) to forecast prices of stocks, currencies, or cryptocurrencies across markets supported by the yahoo_fin library, all with the aid of the versatile Keras library.

To carry  on this data-driven journey, you'll need Python 3.6 and a set of essential libraries. Keras serves as our neural network framework, while scikit-learn, numpy, pandas, and matplotlib play pivotal roles in data manipulation and visualization. Yahoo_fin, the data source, provides real-time market data.

To get started, simply install the required packages using 'pip3 install -r requirements.txt'. The dataset is automatically fetched using yahoo_fin and stored in the data folder, making it convenient for analysis. You can choose from a variety of tickers to explore different markets.

Once you've prepared the environment, you can initiate predictions. An example using Bitcoin (BTC-USD) demonstrates how to train the model, predict prices, make buy or sell decisions, and evaluate model performance. Training logs are stored for review, and you can fine-tune parameters for various markets.

Source Code - Price Prediction

Fruit Image Classification

  • Language: Python
  • Dataset: Image

This project is a captivating endeavor in the realm of computer vision and image classification. By utilizing a wide array of Python libraries and packages, including OpenCV, Keras, and TensorFlow, this project equips beginners with essential skills in image processing, deep learning, and model evaluation. By analyzing and classifying images of different fruits, learners can not only enhance their understanding of image classification but also grasp the nuances of model training and validation.

The project's real-world applicability extends to various domains, from quality control in the agricultural industry to automated inventory management in e-commerce. Through this project you’ll discover how to preprocess images, build Deep Learning models, and evaluate model performance. Insights gained include understanding image augmentation techniques, handling imbalanced datasets, and interpreting classification metrics.

In a world increasingly reliant on visual data, this project empowers beginners to harness the potential of image classification, making it a valuable addition to any data science journey.

Source Code - Fruit Image Classification

Data Science Projects with Source Code for Intermediate Level

As you advance on your data science journey, it's time to level up your skills with 'Data Science Projects with Source Code for Intermediate Level.' Building on the foundational knowledge acquired during your beginner stages, these projects serve as stepping stones to greater complexity and expertise. In this section, we dive into a curated collection of projects designed specifically for those looking to deepen their data science capabilities. Each project not only presents an opportunity to hone your data analysis, machine learning, and data visualization skills but also provides access to the source code, offering invaluable insights into best practices and real-world applications.

Career Guidance ChatBot

  • Language: Python
  • Dataset:  Text, SQL file

This project ventures into the exciting world of natural language processing (NLP) and chatbot development. Utilizing Flask, ChatterBot, NLTK, and various other Python libraries, this project empowers to creation of intelligent chatbots capable of providing career guidance and advice.

The applications of such chatbots are far-reaching, from aiding students in choosing the right career path to assisting job seekers in refining their resumes. By diving into this project, you can enhance their NLP skills, understand the principles of chatbot design, and explore methods for training chatbots using both predefined and custom datasets.

Insights gained include knowledge of text tokenization, chatbot training techniques, and the development of user-friendly web interfaces. As the world increasingly turns to AI-driven chatbots for assistance, this project equips with valuable skills at the intersection of data science and artificial intelligence.

Source Code - Career Guidance ChatBot

Bank Customer Churn Prediction

This project offers a deep dive into predictive analytics, focusing on the critical challenge of customer retention in the banking sector. Leveraging Python libraries such as NumPy, pandas, Matplotlib, and Seaborn, this project equips intermediate data scientists with the skills needed to tackle customer churn prediction.

In the fiercely competitive banking industry, understanding and preventing customer churn is paramount. This project enables learners to explore the factors contributing to customer attrition, visualize key insights, and build predictive models to identify potential churners. It provides practical experience in data wrangling, data exploration, and model fitting.

Insights gained encompass data preprocessing techniques, model selection (including logistic regression, SVM, and ensemble models), and the importance of metrics like recall and precision in the context of imbalanced datasets. The ability to predict customer churn accurately can empower banks to take proactive measures, thereby reducing attrition and improving customer relationships.

This project serves as a bridge between intermediate data science skills and real-world applications in the banking sector, making it a valuable asset for those aspiring to work on data-driven solutions in the financial industry.

Source Code - Bank Customer Churn Prediction

Twitter Sentiment Analysis

This project delves into the fascinating domain of natural language processing (NLP) and sentiment analysis using Python. By harnessing libraries like pandas, Matplotlib, Scikit-learn, Keras, nltk, and Word2Vec, this project enables intermediate data scientists to analyze and classify sentiments expressed in tweets.

In today's digitally connected world, understanding public sentiment is pivotal for businesses and policymakers. This project equips learners to preprocess text data, build deep learning models, and evaluate sentiment classification. Practical applications range from monitoring brand sentiment to gauging public opinion on social issues.

Insights gained include text tokenization, label encoding, word embedding, model construction, training, and evaluation. By predicting sentiments, data scientists can provide valuable insights for decision-making in diverse domains. Whether you're interested in marketing, social analytics, or public opinion research, this project offers a solid foundation for leveraging NLP in data-driven solutions."

This project bridges the gap between intermediate data science skills and real-world applications in sentiment analysis, making it a valuable resource for aspiring data scientists in various domains.

Source Code - Twitter Sentiment Analysis

Uber Data Analysis

  • Language: Python
  • Dataset: CSV file

The "Uber Data Analysis" project is one the top data science real time projects that offers an excellent opportunity for intermediate data scientists to dive into real-world data analysis. By using libraries such as NumPy, Pandas, Matplotlib, and Seaborn, you'll explore and analyze Uber's ridership data in New York City from September 2014 to August 2015.

This project provides insights into Uber's growth patterns, demand characterization, market valuation, and revenue trends. It also attempts to make predictions about future demand growth. Through this project, you can gain hands-on experience in time series analysis and data visualization while uncovering valuable insights into the dynamics of a leading ride-sharing service in one of the world's busiest cities.

Source Code - Uber Data Analysis

Data Science Projects with Source Code for Advanced Level

As you ascend to the advanced level in your data science journey, it's time to tackle complex challenges and deepen your expertise in data science projects for final year. In this section, we will discuss data science projects for final year with source code and you'll find a curated selection of projects that not only demand a high degree of technical skill but also provide access to the source code, offering a deep dive into advanced methodologies. Each project is a stepping stone to mastery, covering domains from data science.

As you embark on these advanced projects for data science, you'll gain invaluable insights into advanced data manipulation techniques, model optimization, and even deployment strategies. Whether your passion lies in developing cutting-edge AI applications or solving complex real-world problems, these projects are your gateway to the pinnacle of data science expertise.

Classification of Galaxies, Stars and Quasars based on the RD14 from the SDSS

The project, "Classification of Galaxies, Stars, and Quasars based on the RD14 from the SDSS," is a valuable resource for advance level data science enthusiasts. It offers insight into the world of astronomy and astrophysical classification, providing hands-on experience with real-world astronomical data. By working on this project, you'll gain expertise in handling large and complex datasets, performing feature engineering, and implementing various machine learning models like Support Vector Machines, Random Forests, and XGBoost. You'll also delve into multivariate analysis, cross-validation techniques, and feature importance visualization. Ultimately, this project bridges the gap between data science and astrophysics, making it an exciting and educational endeavor for those looking to expand their data science skills.

Source Code - Classification of Galaxies, Stars and Quasars

Customer Segmentation

The "Customer Segmentation" project is an advanced python data science project that provides valuable insights into customer behavior analysis within the realm of e-commerce. Utilizing libraries like Pandas, Matplotlib, Seaborn, and scikit-learn, this project involves data preprocessing, clustering, and classification. It delves into variables such as countries, product categories, and customer behavior.

By training various machine learning models, including Support Vector Machines, Decision Trees, and Random Forests, you can classify customers into distinct categories based on their purchasing habits. This project offers hands-on experience with real-world data and the opportunity to refine your data science skills while uncovering key insights into customer segmentation and behavior in e-commerce.

Source Code - Customer Segmentation

Climate Change Forecast

  • Language: Python
  • Dataset: CSV file

The "Climate Change Forecast" project is one of the example of real-world data science projects. It is a valuable resource for advanced level data scientists interested in time series modeling and forecasting. By utilizing libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Statsmodels, this project delves into the analysis and prediction of climate change patterns in Rio de Janeiro.

It employs Seasonal ARIMA models to capture seasonality and trends in climate data, providing practical experience in handling real-world climate data and developing predictive models.

Key insights include understanding time series modeling, stationarity, model evaluation using metrics like RMSE, and extrapolation for future predictions. These skills are broadly applicable in fields such as finance and environmental science.

Source Code - Climate Change Forecast


In conclusion, the projects discussed in this article offer valuable learning opportunities in data science. Beginners can start with projects like "Fruit Classification," while intermediate learners can explore "Customer Segmentation" and "Twitter Sentiment Analysis" to grasp more advanced concepts. Aspiring data scientists can gain insights into market analysis with "Uber Data Analysis" and dive into complex techniques like deep learning with "Classification of Galaxies, Stars, and Quasars" or "Climate Change Forecast." These projects provide hands-on experience, helping you build a strong portfolio, resumes and stand out in the competitive field of data science. So, you can also choose from kaggle data science projects that suits your skill level and interests, and embark on a rewarding journey of learning and add these data science projects for resume to land your dream job in data science.

To boost your data science skills and career prospects, consider a Master's in Data Science or a Data Science certification. These programs offer a structured path to expertise and industry value. Choose projects and education for a rewarding journey toward your dream data science job. Happy coding!

Frequently asked Questions

1: What programming languages are commonly used in data science projects?

Answer: Python and R are the most popular programming languages for data science projects. Python's versatile libraries like NumPy, Pandas, Matplotlib, and scikit-learn make it a go-to choice. R is known for its strong statistical capabilities and visualization libraries like ggplot2.

2: How do I handle missing data in my dataset for data science projects?

Answer: Handling missing data is crucial. You can choose to remove rows with missing values, impute missing values with mean/median/mode, or use advanced techniques like predictive modeling to fill in missing values. The approach depends on the nature of your data and the impact of missing values on your analysis.

3: How can I improve the performance of my machine learning model?

Answer: There are several ways to improve model performance. You can try feature engineering to create more relevant features, fine-tune hyperparameters, use more advanced algorithms, address overfitting by adjusting regularization parameters, and increase training data. Cross-validation and grid search can help identify the best combination of hyperparameters for optimal performance.

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