🧠 Top 10 Python Data Science Projects for Beginners (With Code)
🔰 Introduction
Are you starting your journey in data science with Python? One of the best ways to learn is by creating practical projects. These projects not only teach you essential skills like data cleaning, visualization, and machine learning, but also help you build a strong portfolio to impress future employers.
In this blog, you will find 10 useful Python data science projects for beginners – each with an explanation of what you will learn, the tools used, and links to datasets or sample code.
📚 Why Start with Projects in Data Science?
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Just theoretical knowledge is not enough – projects apply your learned knowledge to real-world problems.
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You will build a strong GitHub portfolio to showcase your skills.
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A great way to learn tools like Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and more.
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Projects enhance your resume and give you an edge in internships, interviews, and hackathons.
🔟 Top 10 Python Data Science Projects for Beginners
1. 🌤️ Weather Data Analysis
What you will learn: Data cleaning, datetime parsing, line plots, grouping by date.
Libraries: pandas, matplotlib, seaborn
Dataset: OpenWeatherMap, Kaggle Weather Dataset
Goal: Analyze temperature trends, humidity, and precipitation across different cities or time periods.
🔗 Additional tip: Add rolling average trends for visual clarity.
2. 🎬 Movie Recommendation System
What you will learn: Text similarity, cosine similarity, vectorization.
Libraries: pandas, scikit-learn
Dataset: TMDB 5000 Movies Dataset on Kaggle
Goal: Recommend similar movies based on user’s input (title).
🔗 Bonus tip: Convert it to a Streamlit app to make it interactive.
3. 🦠 COVID-19 Data Tracker
What you will learn: Time-series visualization, cumulative data, mapping.
Libraries: Pandas, Plotly, Seaborn
Dataset: Johns Hopkins GitHub repository
Goal: Visualize the growth of cases in different countries and predict future trends.
🔗 Bonus tip: Add country-wise comparisons or death-to-recovery ratios.
4. 🎓 Student Performance Predictor
What you will learn: Regression modeling, feature selection.
Libraries: Scikit-learn, Pandas, NumPy
Dataset: UCI Student Performance Dataset
Goal: Predicting students’ grades based on study time, absences, and other inputs.
🔗 Additional tip: Add accuracy scores using MAE or R2.
5. 🏭 Air Quality Index Analysis
What you will learn: Data visualization, heatmaps, time-based filtering.
Libraries: pandas, seaborn, matplotlib
Dataset: OpenAQ, Kaggle Air Quality Dataset
Goal: Analyze the level of pollutants in cities over time and find the most polluted areas.
🔗 Additional suggestion: Add interactive filtering with Plotly.
6. 🚢 Titanic Survival Prediction
What you will learn: Classification using logistic regression, preprocessing.
Libraries: pandas, scikit-learn
Dataset: Titanic Dataset on Kaggle
Goal: Predict survivors in the Titanic disaster using passenger data.
🔗 Additional suggestion: Use OneHotEncoder and use decision trees.
7. 📺 YouTube Trending Videos Analysis
What you will learn: EDA (Exploratory Data Analysis), text analysis, sentiment scoring.
Libraries: pandas, seaborn, textblob
Dataset: Trending YouTube videos on Kaggle
Goal: Find trends such as most liked categories, longest videos, or most disliked creators.
🔗 Additional tip: Add bar plots or word clouds of tags and titles.
8. 📽️ Netflix Shows Data Analysis
What you will learn: Grouping, filtering, EDA.
Libraries: pandas, matplotlib, seaborn
Dataset: Netflix dataset on Kaggle
Goal: Find the most common genres, trends of content releases, and compare movies and TV shows.
🔗 Bonus tip: Create time-series plots for year-wise content releases.
9. 🚨 Fake News Detection
What you will learn: Natural Language Processing (NLP), TF-IDF, Classification.
Libraries: Scikit-learn, Pandas, NLTK
Dataset: Fake News Dataset on Kaggle
Goal: Predict whether a news article is fake or real based on its text.
🔗 Bonus tip: Try using different vectorizers (TF-IDF vs CountVectorizer).
10. 🏡 House Price Prediction
What you will learn: Regression, Feature Engineering, Outlier Detection.
Libraries: Pandas, Scikit-learn, Matplotlib
Dataset: Ames Housing Dataset
Goal: Predict the prices of houses using multiple features such as size, location, and year.
🔗 Additional tip: Apply a log transformation to the price column for better results.
⚒️ Tools and Libraries Used in These Projects
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📊 Pandas and NumPy: Data manipulation
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📈 Matplotlib and Seaborn: Data visualization
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🤖 Scikit-learn: Machine learning
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📝 Jupyter Notebook: Writing and testing code
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🌐 Kaggle/Open Datasets: Real-world data sources
🚀 Tips to Get the Most Out of These Projects
✅ Start small: Choose 1-2 projects and complete them completely.
✅ Customize: Rearrange the code and visuals, experiment with new datasets, and personalize the approach.