After completing the Titanic Kaggle project, it’s a good idea to choose another beginner-friendly project that builds on your foundational data science skills. Here are some great second projects to consider on Kaggle:
1. House Prices: Advanced Regression Techniques
Why it’s good for you:
- Introduces regression techniques.
- Helps you practice feature engineering, handling missing data, and data transformations.
- Explores advanced topics like log transformation and interaction terms.
- Excellent for learning how to predict continuous variables.
Skills you’ll develop:
- Regression modeling.
- Feature selection.
- Handling skewed data and outliers.
Link: House Prices Competition
2. Zillow’s Home Value Prediction
Why it’s good for you:
- Continuation of working with real estate data.
- Focuses on handling larger datasets and understanding real-world prediction challenges.
Skills you’ll develop:
- Advanced regression.
- Working with time-series-like data.
3. Digit Recognizer (MNIST Data)
Why it’s good for you:
- Introduces image recognition and machine learning techniques.
- Explores classification problems with clear goals.
Skills you’ll develop:
- Working with image datasets.
- Neural networks and deep learning basics (if you use TensorFlow/Keras).
Link: Digit Recognizer
4. Predicting Loan Default (Loan Prediction Dataset)
Why it’s good for you:
- Focuses on classification and business analytics.
- Helps you learn to interpret results in business contexts.
Skills you’ll develop:
- Logistic regression, decision trees, and ensemble techniques.
- Understanding categorical variables and their impact.
Link: Loan Prediction
5. Heart Disease Prediction
Why it’s good for you:
- Provides medical data to analyze, which is becoming increasingly popular in data science.
- Encourages exploratory data analysis (EDA) and feature importance.
Skills you’ll develop:
- Classification problems.
- Feature selection and understanding correlations.
Link: Heart Disease Dataset
6. Pima Indians Diabetes Dataset
Why it’s good for you:
- Small dataset focused on binary classification.
- Ideal for practicing machine learning pipelines.
Skills you’ll develop:
- Binary classification using logistic regression or decision trees.
- Model evaluation with precision, recall, and F1-score.
Link: Pima Indians Diabetes Dataset
7. Mall Customers Segmentation Dataset
Why it’s good for you:
- Introduces unsupervised learning concepts like clustering.
- Helps you practice visualizations and interpret group behavior.
Skills you’ll develop:
- K-means clustering.
- Data visualization and grouping analysis.
Link: Mall Customer Segmentation
