20+ Data Science Projects in Python for Every Skill Level

Data science involves studying intricate data sets to find patterns which generate valuable insights that help make correct decisions and Python stands as the preferred tool because of its simplicity and broad capabilities and large library collection. Learning these skills must be fun and efficient if you directly work in real life Data Science Projects in Python where you can apply theory and play with real datasets. 

We are in the world of technology, where innovation is moving at the speed of light, every day it brings a new breakthrough. Now, tech is changing our way of living, our working process and how we think. From AI to IOT, Cloud Computing to Automation it's our part of daily life. 

In this blog, we will discuss 20+ Data Science Projects in Python with source code, ranging from beginner to advanced. Each project is designed to help you build practical skills and gain confidence in working with data.  

Now, without any delay let’s get started by exploring the Data Science Projects in Python. 

If you’re confident with the basics of Python, you can jump straight into these projects. For those looking for structured learning, upGrad’s Data Science Courses offer a mix of theory and hands-on projects, along with mentorship from experienced instructors and industry experts. 

Top Data Science Projects in Python from Beginners to Advanced 

Here’s a visual overview of top data science projects with source code in Python, from beginner-friendly tasks to advanced challenges. 

Use this as a roadmap to explore different project ideas, practice coding, and gradually build your expertise in Python and data science. Now we will start from beginner level projects. 

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Beginner Level Data Science Projects in Python 

If you are new to Data Science and have a good exposure to Python and libraries used for it, you are ready to explore the Beginner Friendly projects and after this you can jump to the intermediate one. 

1. Fake News Detection 

In this project you will build a smart solution for fake news detection to find out whether the news is fake or real. We will teach a machine learning model to act like a digital fact checker. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 
You will create a machine learning model capable of classifying news as real or fake, gaining hands-on experience in data preprocessing, feature extraction, and model evaluation. 

Check out this Project- Fake News Detection Project Using Python and ML 

2. Detecting Parkinson Disease 

In this project you will make a machine learning model to detect Parkinson's disease symptoms from voice measurements. This offers insights into healthcare analytics and predictive modeling. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Scikit-learn 
• Matplotlib/Seaborn 

Project Outcome: 
You will build a predictive model that identifies Parkinson’s disease from voice data, learning data preprocessing, feature analysis, and classification techniques in healthcare analytics. 

Check out this Project- Detecting Parkinson Disease Project Using Python 

3. Color Detection 

In this project, you will develop a color detection model with Python and OpenCV. It will show the name and RGB values of any color that is clicked on an image. 

Tools and Technologies Used: 

• Python 
• NumPy 
• OpenCV 
• Pandas 

Project Outcome: 

You will create a color detection tool that identifies colors in images, gaining practical experience with OpenCV, image processing, and Python programming. 

Check out this Project- Color Detection Project Using Python 

4. Iris Data Set 

In this project, you will build a machine learning model to classify an iris flower into one of the three species: Setosa, Versicolor, or Virginica, based on the length and width of their sepals and petals. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Scikit-learn 
• Matplotlib/Seaborn 

Project Outcome: 

You will build a model that classifies iris flowers by species, learning data analysis, feature selection, and supervised classification techniques. 

Check out this Project- Iris Dataset Classification Project Using Python 

5. Loan Prediction 

You will create a machine learning model that predicts whether a loan application will be approved or not. In this beginner level project, you will work with real and historical data of prior applicants. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Scikit-learn 
• Matplotlib/Seaborn 

Project Outcome: 

You will gain hands-on experience in data preprocessing, EDA, and training multiple classification models to identify the most accurate predictor for the dataset. 

Check out this Project- Loan Prediction Project: Build a Model to Predict Loan Approvals with Confidence 

6. Sales Data Analysis 

This project analyzes past sales data using Python to identify trends, patterns, and anomalies. It applies moving averages and Prophet to forecast future sales, helping understand sales behavior and support data-driven decisions. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Statsmodels 
• Prophet 

Project Outcome: 

You will learn to analyze sales data, detect trends and anomalies, and build a predictive model for forecasting future sales using Python and Prophet. 

Check out this Project- Sales Data Analysis Project – Learn, Analyze & Drive Business Growth! 

7. Customer Purchase Behavior Analysis 

In this project, you’ll use a commerce store's data to find patterns in what people often buy together and understand what types of shoppers are most valuable to the business. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 

You will perform RFM analysis and K‑Means clustering to understand customer behavior, identify valuable segments, and uncover purchasing patterns using Python. 

Check out this Project- Customer Purchase Behavior Analysis Project Using Python 

8. Airline Passenger Traffic Analysis 

In this project, you will preprocess and analyze monthly passenger data, identify seasonal trends, and forecast future travel demand using a basic prediction model. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 
• Statsmodels 
• Prophet 

Project Outcome: 

You will gain experience in time series analysis, uncover seasonal travel patterns, and build a forecasting model to predict future airline passenger traffic using Python. 

Check out this Project- Complete Airline Passenger Traffic Analysis Project Using Python 

Now that you’ve built a foundation with beginner projects, let’s move on to intermediate-level Data Science Projects in Python that challenge your skills and tackle more complex problems. 

Intermediate Level Data Science Projects in Python 

You are now ready to deal with more complex datasets and apply advanced machine learning and analytical techniques. Let’s Explore these Intermediate level projects. 

9. Crime Rate Prediction 

In this project you will build a machine learning model to analyze crime data, uncover trends, and identify factors affecting crime rates across cities using Python. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 

You will build models to predict crime patterns, perform feature engineering, and visualize data to understand the social and economic influences on crime. 

Check out this Project- Crime Rate Prediction by City Using Python and Machine Learning 

10. Customer Churn Prediction 

This Data Science Project in Python Build a machine learning model to predict customer churn by analyzing behavior and transaction history from real-world datasets. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Seaborn 
• Scikit-learn 

Project Outcome: 

You will be able identify at-risk customers, uncover retention patterns, and gain hands-on experience in predictive modeling for business growth. 

Check out this Project- Customer Churn Prediction Project: From Data to Decisions 

11. Fraud Detection in Transactions 

This Project is all about analyzing real life transaction data using Python to detect fraudulent activities with machine learning and anomaly detection techniques. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Scikit-learn 
• TensorFlow 

Project Outcome: 

You will build models to identify suspicious transactions, apply anomaly detection methods, and gain practical experience in fraud detection using real-world data. 

Check out this Project- Fraud Detection in Transactions with Python: A Machine Learning Project 

12. Handwritten Digit Recognition 

In this project, you’ll solve the handwritten digit recognition problem using the Kaggle Sample dataset, which contains thousands of grayscale images of digits from 0 to 9 using Python and CNNs, tackling a classic computer vision challenge. 

Tools and Technologies Used: 

• Python 
• NumPy 
• Scikit-learn 
• TensorFlow/ PyTorch 

Project Outcome: 

You will build a deep learning model that accurately classifies handwritten digits, gaining hands-on experience in image processing and neural networks. 

Check out this Project- Handwritten Digit Recognition with CNN Using Python 

13. Black Friday Sales Prediction 

Analyze the Black Friday dataset to study customer purchasing behavior and identify factors influencing spending patterns. 

Tools and Technologies Used: 

• Python 
• Pandas 
• Matplotlib/Seaborn 
• DataFrames 

Project Outcome: 

You will uncover insights into consumer habits, build predictive models for sales forecasting, and enhance decision-making with data-driven analysis. 

Check out this Project- Black Friday Dataset Analysis for Sales Prediction 

14. Travel Cost Prediction 

In this Intermediate Data Science Project in Python, you’ll explore the traveler trip dataset to analyze demographics and trip details, then use machine learning to predict overall travel costs. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 
• XGBoost 

Project Outcome: 

You will build a regression model to forecast trip expenses, gaining insights into traveler behavior and helping design optimized travel packages. 

Check out this Project- Predicting Travel Costs Using the Traveler Trip Dataset 

15. Song Recommendation System 

Build a song recommendation system using metadata such as genre, artist, and track name to suggest similar songs from the TCC CEDs Music Dataset. 

Tools and Technologies Used: 

• Python 
• Pandas 
• NumPy 
• Matplotlib/Seaborn 
• Scikit-learn 

Project Outcome: 

You will develop a recommender system that personalizes music suggestions, gaining practical experience in recommendation algorithms and user behavior analysis. 

Check out this Project- Song Recommendation System Using Machine Learning 

16. Sentiment Analysis on IMDB Reviews 

Perform sentiment analysis on IMDB reviews using NLP and machine learning to classify movie reviews as positive or negative. 

Tools and Technologies Used: 

• Python 
• Pandas 
• Scikit-learn 
• NLTK / TextBlob 

Project Outcome: 

You will build a text classification model, apply algorithms like Logistic Regression and Naive Bayes, and gain hands-on experience in natural language processing. 

Check out this Project- Sentiment Analysis on IMDB Reviews Using Machine Learning 

After completing the intermediate projects, it’s time to take your skills to the super next level with advanced projects that deal with more complex datasets and learn data science more efficiently. 

Advanced Data Science Projects in Python 

By working on these advanced projects, you’ll gain expertise in solving real-world problems, mastering deep learning, NLP, and time series analysis with Python. 

Project Name	Tools & Technologies	Project Outcome
Image  Captioning	Python, TensorFlow/Keras, OpenCV, Pre-trained CNNs (VGG, Inception)	Generate captions for images using CV and NLP.
Voice Command Recognition	Python, Librosa, TensorFlow/Keras	Recognize and classify voice commands for automation.
Human Activity Recognition	Python, TensorFlow/Keras, Scikit-learn	Classify human activities from wearable sensor data.
Energy Consumption Forecasting	Python, Pandas, NumPy, LSTM, Matplotlib	Predict energy usage patterns for better resource management.
Air Quality Analysis & Forecasting	Python, Pandas, Matplotlib, Random Forest	Analyze pollutants and forecast air quality trends.
Detecting Natural Disasters	Python, TensorFlow/Keras, Satellite imagery datasets (NASA, USGS)	Identify natural disasters using satellite imagery.

Conclusion 

These data science projects with source code in Python cover beginner, intermediate, and advanced levels, giving you hands-on experience with real-world datasets and machine learning techniques. By practicing them, you can strengthen your skills and build a strong data science portfolio.