Top 21 Kaggle Competitions for Intermediate

Top 21 Kaggle Competitions for Intermediate
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In this blog, we will be Navigating the Data Science Odyssey with the Top 21 Kaggle Competitions for Intermediate Enthusiasts in data science.

Embarking on the intermediate phase of your data science journey? Kaggle, the mecca for data science competitions, presents an array of challenges that cater to intermediate-level practitioners seeking to refine their skills and tackle more complex problems.

In this comprehensive guide, we’ll explore the top 21 Kaggle competitions for intermediate data scientists.

Each competition offers a unique set of challenges, datasets, and opportunities to delve deeper into various domains of data science.

Related Article: Top 21 Kaggle Competitions for Data Science

Top Kaggle Competitions for Intermediate

For those in the intermediate stage of learning data science, the journey through the Top 21 Kaggle Competitions is like an adventure.

Here are the top Top Kaggle Competitions for Intermediate to make the data science and machine learnings journey easy and exciting:

1. IEEE-CIS Fraud Detection

The IEEE-CIS Fraud Detection competition presents a more intricate challenge in predicting fraudulent transactions.

With real-world imbalanced datasets, it provides an excellent opportunity to delve into advanced fraud detection techniques.

Steps:
  1. Imbalanced data handling
  2. Feature engineering
  3. Model selection (e.g., XGBoost, LightGBM)
  4. Hyperparameter tuning for enhanced fraud detection accuracy

IEEE-CIS Fraud Detection

Example:

Explore kernels and solutions that tackle the imbalanced nature of fraud detection datasets effectively.

2. Tabular Playground Series – Feb 2022

The Tabular Playground Series – Feb 2022, offers a challenging tabular dataset for prediction.

It’s an excellent competition for refining skills in feature engineering and model selection.

Steps:
  1. Exploratory Data Analysis (EDA)
  2. Feature engineering
  3. Model selection and evaluation
  4. Ensemble techniques for model stacking

Tabular Playground Series – Feb 2022

Example:

Explore top-performing kernels to understand advanced feature engineering techniques.

3. Bosch Production Line Performance

The Bosch Production Line Performance competition revolves around predicting the performance of manufacturing components.

It introduces challenges in handling diverse data sources and developing robust predictive models.

Steps
  1. Data preprocessing for diverse data sources
  2. Feature engineering
  3. Model development for regression analysis
  4. Handling missing data effectively

Bosch Production Line Performance

Example:

Investigate kernels that showcase strategies for handling missing data and integrating diverse data sources.

4. CommonLit Readability Prize

The CommonLit Readability Prize focuses on predicting the readability of text passages.

It introduces challenges in natural language processing (NLP) and provides an opportunity to work with textual data.

Steps:
  1. Text preprocessing and tokenization
  2. Feature extraction from textual data
  3. Model development for regression analysis
  4. Fine-tuning NLP models for improved performance

CommonLit Readability Prize

Example:

Explore how participants approached the challenge of predicting text readability and enhancing NLP model performance.

5. Jigsaw Multilingual Toxic Comment Classification

The Jigsaw Multilingual Toxic Comment Classification competition challenges participants to identify toxic comments in multiple languages.

It offers insights into multilingual NLP and the nuances of handling offensive language.

Steps:
  1. Multilingual text preprocessing
  2. Model development for multilingual classification
  3. Evaluation metrics for toxicity detection
  4. Ethical considerations in content moderation

Jigsaw Multilingual Toxic Comment Classification

Example:

Investigate kernels that showcase effective approaches to multilingual text classification and content moderation.

6. Jane Street Market Prediction III

The Jane Street Market Prediction III competition is a continuation of the market prediction series, challenging participants to develop models for predicting market movements.

It provides an advanced platform for honing skills in financial data analysis and prediction.

Steps:
  1. Time-series data analysis
  2. Feature engineering for financial data
  3. Model development and optimization
  4. Visualization of results for interpretability

Jane Street Market Prediction III

Example:

Explore innovative strategies and advanced techniques in predicting market movements.

7. Riiid! Answer Correctness Prediction

The Riiid! Answer Correctness Prediction competition challenges participants to predict student performance on educational platforms.

It combines aspects of time-series data and student engagement prediction.

Steps:
  1. Time-series data analysis for educational platforms
  2. Feature engineering for student engagement
  3. Model development for answer correctness prediction
  4. Ethical considerations in educational data science

Riiid! Answer Correctness Prediction

Example:

Investigate how participants approached predicting student performance and engagement in educational settings.

8. Melbourne University AES/MathWorks/NIH Seizure Prediction

This competition focuses on predicting seizures using intracranial EEG recordings.

It presents a complex challenge in biomedical signal processing and predictive modeling for healthcare applications.

Steps:
  1. Time-series data analysis for EEG recordings
  2. Feature extraction from biomedical signals
  3. Model development for seizure prediction
  4. Evaluation metrics for biomedical predictions

Melbourne University AES/MathWorks/NIH Seizure Prediction

Example:

Explore how participants applied signal processing techniques and developed models for seizure prediction.

9. Quora Insincere Questions Classification

The Quora Insincere Questions Classification competition challenges participants to identify insincere questions on the Quora platform.

It addresses the ethical implications of content moderation and provides insights into handling imbalanced datasets.

Steps:
  1. Text preprocessing for question classification
  2. Handling imbalanced datasets
  3. Model development for binary classification
  4. Ethical considerations in content moderation

Quora Insincere Questions Classification

Example:

Investigate how participants balanced model performance with ethical considerations in content moderation.

10. Jane Street Market Prediction IV

The Jane Street Market Prediction IV competition is the latest iteration in the market prediction series.

It challenges participants to develop models for predicting market movements with an emphasis on real-world trading implications.

Steps:
  1. Time-series data analysis
  2. Feature engineering for financial data
  3. Model development and optimization
  4. Visualization of results for interpretability

Jane Street Market Prediction IV

Example:

Explore cutting-edge strategies and innovative approaches in predicting market movements.

11. RANZCR CLiP – Catheter Line Position Challenge

The RANZCR CLiP – Catheter Line Position Challenge focuses on predicting the correct placement of catheter lines in chest X-ray images.

It provides an opportunity to work with medical imaging data and develop models for image classification.

Steps:
  1. Image preprocessing for medical images
  2. CNN architectures for image classification
  3. Evaluation metrics for medical image analysis
  4. Interpretability of model predictions in healthcare

RANZCR CLiP – Catheter Line Position Challenge

Example:

Investigate how participants approached the challenge of classifying catheter line positions in chest X-ray images.

12. Walmart Recruiting – Store Sales Forecasting

The Walmart Recruiting – Store Sales Forecasting competition involves predicting store sales, offering insights into time-series forecasting and demand prediction.

Steps:
  1. Time-series data analysis for sales forecasting
  2. Feature engineering for demand prediction
  3. Model development and optimization
  4. Visualization of results for interpretability

Walmart Recruiting – Store Sales Forecasting

Example:

Explore how participants handled challenges in forecasting store sales and demand.

13. Jane Street Market Prediction V

The Jane Street Market Prediction V competition continues the legacy of market prediction challenges.

It challenges participants to develop models for predicting market movements with a focus on real-world trading applications.

Steps:
  1. Time-series data analysis
  2. Feature engineering for financial data
  3. Model development and optimization
  4. Visualization of results for interpretability

Jane Street Market Prediction V

Example:

Explore evolving strategies and state-of-the-art approaches in predicting market movements.

14. PetFinder.my – Pawpularity Contest

The PetFinder.my – Pawpularity Contest involves predicting the popularity of pet images.

It combines image analysis and regression techniques, offering a unique challenge in predicting subjective measures.

Steps:
  1. Image preprocessing for pet images
  2. Regression analysis for popularity prediction
  3. Feature engineering for image data
  4. Model evaluation considering subjective metrics

PetFinder.my – Pawpularity Contest

Example:

Investigate how participants approached the challenge of predicting the popularity of pet images.

15. Deepfake Detection Challenge

The Deepfake Detection Challenge focuses on identifying deepfake videos.

It introduces challenges in image and video analysis and offers insights into the evolving field of deepfake detection.

Steps:
  1. Video preprocessing and frame extraction
  2. CNN architectures for deepfake detection
  3. Evaluation metrics for video analysis
  4. Ethical considerations in deepfake detection

Deepfake Detection Challenge

Example:

Explore kernels and solutions that address the challenges in detecting deepfake videos.

16. Understanding Clouds from Satellite Images – Cloud Segmentation

This competition focuses specifically on cloud segmentation in satellite images.

It provides a more focused challenge in image segmentation and classification.

Steps:
  1. Image segmentation techniques
  2. CNN architectures for cloud segmentation
  3. Evaluation metrics for cloud detection
  4. Transfer learning for improved segmentation accuracy

Understanding Clouds from Satellite Images – Cloud Segmentation

Example:

Investigate how participants approached the challenge of segmenting clouds in satellite imagery.

17. Histopathologic Cancer Detection

The Histopathologic Cancer Detection competition involves identifying cancerous regions in histopathologic images.

It provides insights into medical image analysis and the challenges associated with cancer detection.

Steps:
  1. Image preprocessing for histopathologic images
  2. CNN architectures for image classification
  3. Evaluation metrics for medical image analysis
  4. Interpretability of model predictions in healthcare

Histopathologic Cancer Detection

Example:

Explore how participants addressed challenges in detecting cancerous regions in histopathologic images.

18. Google Landmark Retrieval 2020

The Google Landmark Retrieval 2020 competition involves retrieving relevant landmarks from a vast database of images.

It introduces challenges in image retrieval and similarity matching.

Steps:
  1. Image retrieval techniques
  2. Feature extraction for landmark matching
  3. Evaluation metrics for image retrieval
  4. Transfer learning for improved retrieval accuracy

Google Landmark Retrieval 2020

Example:

Investigate how participants tackled challenges in retrieving relevant landmarks from large image databases.

19. Web Traffic Time Series Forecasting

The Web Traffic Time Series Forecasting competition focuses on predicting web traffic for various Wikipedia pages.

It provides an opportunity to refine skills in time-series forecasting and handle diverse data sources.

Steps:
  1. Time-series data analysis for web traffic
  2. Feature engineering for forecasting
  3. Model development and optimization
  4. Visualization of results for interpretability

Web Traffic Time Series Forecasting

Example:

Explore how participants addressed challenges in forecasting web traffic for diverse Wikipedia pages.

20. Lantern: Kaggle’s YouTube Video Prediction Challenge

The Lantern competition involves predicting the number of views for Kaggle YouTube videos.

It provides a unique challenge in time-series forecasting and regression analysis.

Steps:
  1. Time-series data analysis for video views
  2. Feature engineering for regression analysis
  3. Model development and optimization
  4. Visualization of results for interpretability

Lantern: Kaggle’s YouTube Video Prediction Challenge

Example:

Investigate how participants approached the challenge of predicting the number of views for Kaggle YouTube videos.

21. YouTube-8M Video Understanding Challenge

The YouTube-8M Video Understanding Challenge involves predicting the labels for video segments.

It provides an advanced platform for honing skills in video analysis and classification.

Steps:
  • Video preprocessing and frame extraction
  • CNN architectures for video classification
  • Evaluation metrics for video analysis
  • Transfer learning for improved classification accuracy

YouTube-8M Video Understanding Challenge

Example:

Explore kernels and solutions that address the challenges in classifying video segments.

Conclusion

In conclusion, the journey through the top 25 Kaggle competitions for intermediate data scientists is a pivotal step towards mastery in the field of data science.

These competitions offer a rich tapestry of challenges that go beyond the basics, pushing participants to refine their skills, engage with diverse datasets, and develop a nuanced understanding of real-world problems.

The experience gained from these competitions contributes not only to technical proficiency but also to the development of a problem-solving mindset, ethical considerations, and a collaborative spirit within the Kaggle community.

As participants navigate through these challenges, they not only enhance their professional acumen but also contribute to the broader landscape of data science innovation.

The road to mastery is dynamic, and Kaggle competitions serve as a compass, guiding data scientists toward continuous learning, growth, and the fulfillment of their potential in this ever-evolving field.

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