Consolidated Kaggle datasets for learning data science
Embark on Your Data Science Journey through In-Depth Projects and Hands-on Learning
Data science, as an emerging field, is constantly evolving and bringing forth innovative solutions to complex problems. The advent of generative AI models like ChatGPT has made this an even more exciting time to delve into data science. This blog post aims to provide you with a step-by-step guide to kickstart your data science journey, with a focus on learning through comprehensive projects and hands-on experiences.
Pre-requisite: Programming Language
To begin, it’s crucial to have a basic understanding of a programming language, and Python is the perfect choice due to its simplicity and extensive libraries. To get started, check out this YouTube crash course (I totally recommend it):
After that, try your hand at a few easy LeetCode problems to get comfy with coding. Trust me, you’ll want to sharpen your software engineering skills for the long haul.
Project-based Learning
The best way to learn data science is by getting your hands dirty with projects. I’ve handpicked a few Kaggle projects covering a range of data science concepts. For each of these projects, I highly encourage you to snoop around other users’ notebooks and learn from their experiences, too.
Titanic Dataset
Project link: https://www.kaggle.com/competitions/titanic
This is one of the classic datasets with tons of good notebooks for you to learn from. In this project:
- First, get the gist of the problem and the data.
- Explore the data (EDA) and spot patterns and missing values. Get to know libraries like Pandas, Seaborn, and NumPy.
- Fill in the blanks and create new features by combining existing ones (feature engineering).
- Split the dataset and apply simple machine learning models (like logistic regression or decision trees) to predict survival rates.
- Evaluate your model and tweak it. Play around with different algorithms and feature engineering techniques.
Fake and Real News Dataset
Project link: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
- Get a feel for the problem and what makes news fake or real.
- Visualize word frequencies, distributions, and other cool stuff in the text (EDA).
- Learn to clean and preprocess text by removing stopwords, special characters, and applying stemming or lemmatizing.
- Convert text into numbers using techniques like Bag of Words or TF-IDF.
- Try out simple machine learning models (like Naive Bayes or logistic regression) to classify fake and real news. Mix it up with different models and settings.
- Evaluate your model using metrics like accuracy, precision, recall, and F1-score. And answer this question, which metrics is more important for the stakeholders?
Heart Attack Prediction
- Project link: https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset
- Prepare the data for neural network
- Split the dataset and create a simple neural network model using a library like TensorFlow or PyTorch. (I recommend starting with TensorFlow. Tensorflow is more beginner-friendly but Pytorch is more widely used in the industry for more complex models)
- Train the model and evaluate its performance. Experiment with different architectures, activation functions, and optimization techniques.
Fashion MNIST
- Project link: https://www.kaggle.com/datasets/zalando-research/fashionmnist
- Get curious and explore the image data (EDA) to see the distribution of classes, image dimensions, and other cool stuff.
- Learn the basics of neural networks for image datasets and how convolutional neural networks (CNNs) work their magic in image classification tasks.
- Prep the data by normalizing pixel values, reshaping images, and encoding class labels.
- Divide the data into training, validation, and testing sets.
- Craft a CNN model with TensorFlow or PyTorch. Start simple and build up the complexity as you get the hang of it.
- Train the model and keep an eye on its performance on the validation set. Tweak hyperparameters, try different architectures, and use regularization techniques to make them better.
- Test your final model and dig into the results, including confusion matrices and classification reports.
E-mail Classification
- Project link: https://www.kaggle.com/datasets/datatattle/email-classification-nlp
- Investigate the text data (EDA) to spot trends, word frequencies, and other fun stats.
- Learn to clean and preprocess text by removing stopwords, and special characters and applying stemming or lemmatization.
- Turn your text data into numbers using techniques like word embeddings (Word2Vec or GloVe) or Bag of Words.
- Split the dataset into training, validation, and testing sets.
- Dive deeper into neural networks for NLP by learning about models like RNNs and LSTMs.
- Create an RNN or LSTM model with TensorFlow or PyTorch and train it on the data.
- Monitor how well it’s doing on the validation set and fine-tune hyperparameters or experiment with different architectures.
- Evaluate your final model on the test data and analyze the results with metrics like accuracy, precision, recall, and F1-score.
Conclusion
Embarking on your data science journey can be a wild ride, but trust me, it’s so worth it. By following this step-by-step guide and diving deep into project-based learning, you’ll build a solid data science foundation while getting hands-on experience. Just remember, success comes from persistence and learning from your mistakes. Embrace the challenges and let your passion for data science guide you on this amazing adventure. You’ve got this!
Also, let’s make this journey even more exciting by inviting others to contribute their favourite Kaggle projects for beginners. The more, the merrier! If you’ve come across some cool projects that helped you learn data science, feel free to share them in the comments. This way, we can all grow together and support each other in our learning journeys. So, let’s join forces and create an awesome list of beginner-friendly Kaggle projects for everyone to enjoy. Happy learning!
