Compiling Most recent Tabular Data Competitions and Links to Top Kernel here . This competition involves tabular data and lot of ideas can be taken from Past Tabular Data Competitions , notice that I have taken competitions involving large teams like >3K Teams .
\n1 . IEEE-CIS Fraud Detection:Aim is to predict fraud transactions basically a binary classification problem, EDA and Modelling Kernel can be found here , more extensive EDA and Hyper parameter tuning kernel can be found here
\n2. Santander Customer Transaction Prediction: Aim is to predict which customers are likely to make transaction in future , EDA and Prediction Kernel can be found here , Magic Feature and 200 Models kernel can be found here
\n3.ELO Merchant Category Recommendation: Aim is to predict customer loyalty , Basic EDA and Lightgbm kernel can be found here , More detailed EDA and Modelling Kernel can be found here
\n4.Home Credit Default Risk Competition , Aim is to predict which loan applicants will be able to repay loan in future , Feature Engineering kernel can be found here , Feature Importance Kernel can be found here , LightGBM with simple Feature Engineering Kernel can be found here
\n5. RIIID Answer Correctness Prediction Competition : Here Task is to predict probability of student answering next question given his records of previous questions, Comprehensive EDA and Baseline Kernel can be found here , Lightgbm kernel can be found here
\nThanks for Reading
\nHappy Kaggling !
Compiling Most recent Tabular Data Competitions and Links to Top Kernel here . This competition involves tabular data and lot of ideas can be taken from Past Tabular Data Competitions , notice that I have taken competitions involving large teams like >3K Teams .
\n1 . IEEE-CIS Fraud Detection:Aim is to predict fraud transactions basically a binary classification problem, EDA and Modelling Kernel can be found here , more extensive EDA and Hyper parameter tuning kernel can be found here
\n2. Santander Customer Transaction Prediction: Aim is to predict which customers are likely to make transaction in future , EDA and Prediction Kernel can be found here , Magic Feature and 200 Models kernel can be found here
\n3.ELO Merchant Category Recommendation: Aim is to predict customer loyalty , Basic EDA and Lightgbm kernel can be found here , More detailed EDA and Modelling Kernel can be found here
\n4.Home Credit Default Risk Competition , Aim is to predict which loan applicants will be able to repay loan in future , Feature Engineering kernel can be found here , Feature Importance Kernel can be found here , LightGBM with simple Feature Engineering Kernel can be found here
\n5. RIIID Answer Correctness Prediction Competition : Here Task is to predict probability of student answering next question given his records of previous questions, Comprehensive EDA and Baseline Kernel can be found here , Lightgbm kernel can be found here
\nThanks for Reading
\nHappy Kaggling !
Nice. Upvoted. Do take a look at my notebooks and give feedback.
", "rawMarkdown": "Nice. Upvoted. Do take a look at my notebooks and give feedback.", "votes": null }, { "id": "1353817", "postDate": "06/17/2021 08:55:08", "content": "great job👍👍👍👍👍👍👍
", "rawMarkdown": "great job👍👍👍👍👍👍👍", "votes": null } ], "comments": [ { "id": 1346003, "author_name": "arnab132", "author_url": "", "post_date": "06/12/2021 03:19:09", "content": "Nice. Upvoted. Do take a look at my notebooks and give feedback.
", "votes": null, "replies": [] }, { "id": 1353817, "author_name": "daicongxmu", "author_url": "", "post_date": "06/17/2021 08:55:08", "content": "great job👍👍👍👍👍👍👍
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1344728": "Compiling Most recent Tabular Data Competitions and Links to Top Kernel here . This competition involves tabular data and lot of ideas can be taken from Past Tabular Data Competitions , notice that I have taken competitions involving large teams like >3K Teams .\n\n**[1 . IEEE-CIS Fraud Detection](https://www.kaggle.com/c/ieee-fraud-detection):**Aim is to predict fraud transactions basically a binary classification problem, EDA and Modelling Kernel can be found [here ](https://www.kaggle.com/artgor/eda-and-models) , more extensive EDA and Hyper parameter tuning kernel can be found [here](https://www.kaggle.com/kabure/extensive-eda-and-modeling-xgb-hyperopt)\n\n**[2. Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction):** Aim is to predict which customers are likely to make transaction in future , EDA and Prediction Kernel can be found [here](https://www.kaggle.com/gpreda/santander-eda-and-prediction) , Magic Feature and 200 Models kernel can be found [here](https://www.kaggle.com/cdeotte/200-magical-models-santander-0-920)\n\n**[3.ELO Merchant Category Recommendation](https://www.kaggle.com/c/elo-merchant-category-recommendation):** Aim is to predict customer loyalty , Basic EDA and Lightgbm kernel can be found [here](https://www.kaggle.com/fabiendaniel/elo-world) , More detailed EDA and Modelling Kernel can be found [here](https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-elo)\n\n\n**[4.Home Credit Default Risk Competition](https://www.kaggle.com/c/home-credit-default-risk)** , Aim is to predict which loan applicants will be able to repay loan in future , Feature Engineering kernel can be found [here](https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering) , Feature Importance Kernel can be found [here](https://www.kaggle.com/ogrellier/feature-selection-with-null-importances) , LightGBM with simple Feature Engineering Kernel can be found [here](https://www.kaggle.com/jsaguiar/lightgbm-with-simple-features)\n\n\n**[5. RIIID Answer Correctness Prediction Competition ](https://www.kaggle.com/c/riiid-test-answer-prediction):** Here Task is to predict probability of student answering next question given his records of previous questions, Comprehensive EDA and Baseline Kernel can be found [here](https://www.kaggle.com/erikbruin/riiid-comprehensive-eda-baseline) , Lightgbm kernel can be found [here](https://www.kaggle.com/ragnar123/riiid-model-lgbm)\n\nThanks for Reading \nHappy Kaggling !", "1346003": "Nice. Upvoted. Do take a look at my notebooks and give feedback.", "1353817": "great job👍👍👍👍👍👍👍" }, "source": "meta" }