Beginner Projects

Hands-on projects to apply what you've learned in the foundational modules. All six projects include runnable code or notebooks in the repo.

Timing: Start the first beginner project after Stage 2 (modules 02–05) while continuing later modules, you do not need to wait for Stage 9. See START-HERE.md.

Prerequisites

Before starting these projects, you should have completed:

• Module 00: Prerequisites
• Module 01: Python for Data Science
• Module 02: Introduction to ML
• Module 03: Supervised Learning. Regression
• Module 04: Supervised Learning. Classification

ML for beginners curriculum map (projects)

Use these projects to implement algorithms in context (regression, classification, cleaning, feature work). Cross-check concepts with the intro map.

Projects

Project 1: House Price Prediction

Difficulty: Beginner
Time: 2-3 days
Skills: Regression, Feature Engineering, EDA

Predict house prices using features like size, location, number of rooms, etc.

What you'll learn:

• Data cleaning and preprocessing
• Feature engineering
• Linear regression
• Model evaluation

Dataset: California Housing Prices or Kaggle House Prices

Project 2: Iris Flower Classification

Difficulty: Beginner
Time: 1 day
Skills: Classification, EDA, Multiple Algorithms

Classify iris flowers into three species using petal and sepal measurements.

What you'll learn:

• Exploratory data analysis
• Multiple classification algorithms
• Model comparison
• Visualization

Dataset: Built into scikit-learn (load_iris())

Project 3: Titanic Survival Prediction

Difficulty: Beginner
Time: 2-3 days
Skills: Classification, Feature Engineering, Data Cleaning

Predict which passengers survived the Titanic disaster.

What you'll learn:

• Handling missing data
• Categorical encoding
• Feature engineering
• Multiple classification models

Dataset: Kaggle Titanic

Project 4: Spam Email Detection

Difficulty: Beginner
Time: 2-3 days
Skills: Text Classification, NLP Basics, Feature Engineering

Classify emails as spam or not spam using text features.

What you'll learn:

• Text preprocessing
• Feature extraction from text
• Classification with text data
• Evaluation metrics

Dataset: Kaggle SMS Spam Collection

Project 5: Wine Quality Prediction

Difficulty: Beginner
Time: 2-3 days
Skills: Regression/Classification, Feature Selection

Predict wine quality based on chemical properties (can be regression or classification).

What you'll learn:

• Feature selection
• Handling imbalanced data
• Model tuning
• Both regression and classification approaches

Dataset: UCI Wine Quality

Project 6: Customer Data Dashboard with Streamlit

Difficulty: Beginner
Time: 2-3 days
Skills: Streamlit, Data Visualization, Interactive Dashboards

Build an interactive dashboard to visualize and analyze customer data using Streamlit.

What you'll learn:

• Build interactive web applications with Streamlit
• Create dynamic visualizations
• Implement user input and filtering
• Design user-friendly dashboards
• Deploy Streamlit applications
• Work with real-world customer data

Dataset: E-commerce Customer Data, Telco Customer Churn, or any customer dataset

Extensions:

• Add machine learning predictions
• Real-time data updates
• Multi-page dashboard
• Advanced visualizations

Project Structure

Each project should include:

project-name/
 README.md              # Project description and instructions
 data/                  # Dataset (or links to download)
 notebooks/             # Jupyter notebooks
    01-exploratory-data-analysis.ipynb
    02-data-preprocessing.ipynb
    03-model-training.ipynb
    04-model-evaluation.ipynb
 src/                   # Source code (if applicable)
 models/                # Saved models
 results/               # Results and visualizations
 requirements.txt       # Project-specific dependencies

Tips for Success

1. Start Simple: Begin with basic models, then improve
2. Explore First: Always do EDA before modeling
3. Iterate: Build, evaluate, improve, repeat
4. Document: Comment your code and explain decisions
5. Visualize: Create plots to understand data and results
6. Compare: Try multiple algorithms and compare results

Learning Outcomes

After completing these projects, you should be able to:

• Clean and preprocess real-world data
• Apply regression and classification algorithms
• Evaluate models appropriately
• Present results clearly
• Handle common data issues (missing values, outliers, etc.)

Additional Resources

• Kaggle Learn: Micro-courses with projects
• UCI ML Repository: More datasets
• Papers with Code: See how others solved problems

Guide:

• Projects Beginner Guide →: How to structure and ship beginner ML projects
• Advanced Topics →: Advanced techniques for improving projects
• Project Tutorial →: Step-by-step Titanic project walkthrough
• Quick Reference →: Quick lookup guide

Ready to start? Pick a project and begin. Start with Iris Classification if you are new. Try House Price Prediction when you want a larger tabular project.

Next Level: 17-projects-intermediate