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https://github.com/the-black-knight-01/Data-Science-Competitions

Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
https://github.com/the-black-knight-01/Data-Science-Competitions
analytics-vidhya competition-code competitive-data-science-github data-science data-science-competition data-science-competitions datahack-competition kaggle kaggle-competition kaggle-competition-for-beginners kaggle-competition-solutions kaggle-solutions-github kaggle-winning-solutions-github machine-learning machinehack-competition xgboost
Last synced: about 1 month ago
JSON representation
Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
Host: GitHub
URL: https://github.com/the-black-knight-01/Data-Science-Competitions
Owner: the-black-knight-01
License: apache-2.0
Created: 2019-08-02T12:59:35.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2020-01-30T13:33:47.000Z (over 4 years ago)
Last Synced: 2024-07-18T11:06:09.304Z (2 months ago)
Topics: analytics-vidhya, competition-code, competitive-data-science-github, data-science, data-science-competition, data-science-competitions, datahack-competition, kaggle, kaggle-competition, kaggle-competition-for-beginners, kaggle-competition-solutions, kaggle-solutions-github, kaggle-winning-solutions-github, machine-learning, machinehack-competition, xgboost
Homepage: https://interviewbubble.com
Size: 105 KB
Stars: 796
Watchers: 54
Forks: 216
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        Please send pull request if you want to add more competition solutions.

For Comment and Suggestions: [Discusssion Thread:](https://gist.github.com/interviewBubble/b1654c18b20b944876e513d953d437fd#file-data-science-competitions-discussion)

# 1. Kaggle

## Regression

#### Elo Merchant Category Recommendation

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82314#latest-525737))

* 7th Place Solution  ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82055#latest-483943))

* 10th Place Solution  ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82093#latest-529125))

* 11th Place Solution  ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82127#latest-502682))

* 19th Place Solution  ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82178#latest-480628))

* 21th Place Solution  ([Explanation](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82235#latest-481035))([code](https://github.com/bestpredicts/ELO))

## Classification 

#### Santander Customer Transaction Prediction

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89003#latest-590312))

* 2nd Place Solution  ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/88939#latest-534927))([code](https://github.com/KazukiOnodera/Santander-Customer-Transaction-Prediction))

* 5th Place Solution  ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/88897#latest-517607))

* 7th Place Solution ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89023#latest-518634))

* 9th Place Solution  ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89302#latest-516440))

* 10th Place Solution  ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/88997#latest-517584))

* 29th Place Solution ([Explanation](https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89034#latest-548982))([code](https://github.com/btrotta/kaggle-santander-2019))

#### PetFinder.my Adoption Prediction

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/102099#latest-589409))([code](https://www.kaggle.com/naka2ka/stack-480-speedup-groupkfold-with-no-dict))

* 3rd Place Solution  ([Explanation & code](https://www.kaggle.com/wuyhbb/final-small))

* 6th Place Solution  ([Explanation & code](https://www.kaggle.com/bminixhofer/6th-place-solution-code))

* 8th Place Solution ([Explanation & code](https://www.kaggle.com/adityaecdrid/8th-place-solution-code))

* 10th Place Solution  ([Explanation & code](https://www.kaggle.com/chizhu2018/final-submit-two-10th-solution-private-0-442))

* Place Solution  ([Explanation & code](https://www.kaggle.com/corochann/13-th-place-solution-ensemble-of-5-models))

#### Santander Product Recommendation

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26835#latest-549998))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26824#latest-379386))([code](https://github.com/ttvand/Santander-Product-Recommendation))([Blog](https://ttvand.github.io/Second-place-in-the-Santander-product-Recommendation-Kaggle-competition/))

* 3rd Place Solution - R language ([Explanation & code](https://www.kaggle.com/c/santander-product-recommendation/discussion/26899#latest-385293))

* 4th Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26845#latest-549966))

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26841#latest-152148))([code](https://github.com/jturkewitz/SideProjects/tree/master/Kaggle/Santander_Prod))

* 8th Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26838#latest-153042))([code](https://github.com/yaxinus/santander-product-recommendation-8th-place))

* 11th Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26823#latest-180009))([code](https://github.com/rohanrao91/Kaggle_SantanderProductRecommendation))

## Text Classification 

#### Jigsaw Unintended Bias in Toxicity Classification

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/100661#latest-590437))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/97471#latest-582610))

* 4th Place Solution  ([Explanation](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/101927#latest-590658))([code](https://github.com/iezepov/combat-wombat-bias-in-toxicity))([Kaggle Kernel](https://www.kaggle.com/iezepov/wombat-inference-kernel))

* 8th Place Solution  ([Explanation](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/discussion/100961#latest-586393))([code](https://www.kaggle.com/haqishen/jigsaw-predict))

#### Quora Insincere Questions Classification

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/80568#latest-570793))

* 2nd Place Solution  ([Explanation](https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/81137#latest-552221))

* 3rd Place Solution  ([Explanation](https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/80495#latest-548808))([code](https://www.kaggle.com/wowfattie/3rd-place))

* 4th Place Solution ([Explanation](https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/81632#latest-533550))([code](https://www.kaggle.com/kfujikawa/4th-place))

* 7th Place Solution  ([Explanation](https://www.kaggle.com/c/quora-insincere-questions-classification/discussion/80561#latest-582439))

#### Quora Question Pairs

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/quora-question-pairs/discussion/34355#latest-572705))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/quora-question-pairs/discussion/34310#latest-450693))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/quora-question-pairs/discussion/34288#latest-339489))

* 7th Place Solution ([Explanation](https://www.kaggle.com/c/quora-question-pairs/discussion/34697#latest-349346))

* Some interesting solutions from the web ([write-up](https://www.kaggle.com/c/quora-question-pairs/discussion/30260#latest-221245))

* 24 Place Solution ([Explanation](https://www.kaggle.com/c/quora-question-pairs/discussion/34534#latest-420654))([code](https://github.com/aerdem4/kaggle-quora-dup))

#### Toxic Comment Classification Challenge

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52557#latest-533843))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52612#latest-413355))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52762#latest-434841))

* 27 Place Solution ([Explanation](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52719))([code](https://github.com/zake7749/DeepToxic))

* Collection of winning solutions ([write-up](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/72597#latest-437366))

## Document Classification & Data extraction

#### Tradeshift Text Classification

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/tradeshift-text-classification/discussion/10901#latest-62000))([code](https://github.com/daxiongshu/tradeshift-text-classification))

* 28th Place Solution ([Explanation & code](https://www.kaggle.com/c/tradeshift-text-classification/discussion/10629))

* 71th Place Solution ([code](https://github.com/mandelbrot/kaggle-tradeshift-text-classification))

* Another Solution ([code](https://github.com/jdlevitt/Tradeshift_kaggle))

## Time series analysis 

#### Two Sigma: Using News to Predict Stock Movements

* 49th Place Solution ([Explanation & code](https://www.kaggle.com/silvernine/very-simple-nn-model-market-data-only))

* 91th Place Solution ([Explanation & code](https://www.kaggle.com/alluxia/lb-0-6326-tuned-xgboost-baseline))

* 113th Place Solution ([Explanation & code](https://www.kaggle.com/charleslandau/iterative-approach))

* 119th Place Solution([Explanation & code](https://www.kaggle.com/yatzhash/news-features-without-headline-subjects))

* Post-competition thoughts([write-up](https://www.kaggle.com/c/two-sigma-financial-news/discussion/102914#latest-593302))

#### Web Traffic Time Series Forecasting

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43795))([code](https://github.com/Arturus/kaggle-web-traffic))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/39395))([code](https://github.com/jfpuget/Kaggle/tree/master/WebTrafficPrediction))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/39876))

* 6th Place Solution ([Explanation](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/39370))([code](https://github.com/sjvasquez/web-traffic-forecasting))

* Tips from the winning solutions ([write-up](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43535))

* General Approach ([write-up](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/39367))

#### Rossmann Store Sales

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/rossmann-store-sales/discussion/18024#latest-581685))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/rossmann-store-sales/discussion/17974#latest-477628))([code](https://github.com/entron/entity-embedding-rossmann))

## QnA System:

#### TensorFlow 2.0 Question Answering

* [1st place solution](https://www.kaggle.com/c/tensorflow2-question-answering/discussion/127551)

* [2nd place solution](https://www.kaggle.com/c/tensorflow2-question-answering/discussion/127333)

* [3rd place solution](https://www.kaggle.com/c/tensorflow2-question-answering/discussion/127339)

* [4th place Solution](https://www.kaggle.com/c/tensorflow2-question-answering/discussion/127371)

## Recommendation System:

#### Santander Product Recommendation

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26835#latest-549998

))

* 2nd Place Solution ([Explanation](https://ttvand.github.io/Second-place-in-the-Santander-product-Recommendation-Kaggle-competition/))([code](https://github.com/ttvand/Santander-Product-Recommendation))

* 3rd Place Solution ([Explanation & code](https://www.kaggle.com/c/santander-product-recommendation/discussion/26899#latest-385293))

* 4th Place Solution ([Explanation](https://www.kaggle.com/c/santander-product-recommendation/discussion/26845#latest-549966))

## Coreference Resolution

#### Gendered Pronoun Resolution

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/gendered-pronoun-resolution/discussion/90392#latest-521800))([code](https://github.com/sattree/gap))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/gendered-pronoun-resolution/discussion/90424#latest-522089))

* Place Solution ([Explanation](https://www.kaggle.com/c/gendered-pronoun-resolution/discussion/90484#latest-522332))([code](https://github.com/zake7749/Fill-the-GAP))

* Place Solution ([Explanation](https://www.kaggle.com/c/gendered-pronoun-resolution/discussion/90334#latest-521489))([code](https://github.com/boliu61/gendered-pronoun-resolution))

* Top solutions ([write-up](https://www.kaggle.com/c/gendered-pronoun-resolution/discussion/90339))

## Signal Processing

#### LANL Earthquake Prediction

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/94390))([code](https://www.kaggle.com/ilu000/1-private-lb-kernel-lanl-lgbm/))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/94369))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/94459))

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/94484))

* Gold Medal Solutions ([write-up](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/94361))

## Image Classification 

#### Cdiscount’s Image Classification Challenge

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/cdiscount-image-classification-challenge/discussion/45863))

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/cdiscount-image-classification-challenge/discussion/45733))([Video Tutorial with Eng subtitles](https://www.youtube.com/watch?v=Mw2vdYv4ups&feature=youtu.be))

* 7th Place Solution ([Explanation](https://www.kaggle.com/c/cdiscount-image-classification-challenge/discussion/45737#latest-327941))

* 8th Place Solution ([Explanation](https://www.kaggle.com/c/cdiscount-image-classification-challenge/discussion/45850))

* 9th Place Solution ([Explanation](https://www.kaggle.com/c/cdiscount-image-classification-challenge/discussion/45721#latest-327936))

#### Right Whale Recognition

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/noaa-right-whale-recognition/discussion/18409#latest-170541))([code](https://www.dropbox.com/s/rohrc1btslxwxzr/deepsense-whales.zip?dl=1))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/noaa-right-whale-recognition/discussion/18325#latest-104535))([code](https://github.com/felixlaumon/kaggle-right-whale))

## Video Challenge

#### The 3rd YouTube-8M Video Understanding Challenge

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/youtube8m-2019/discussion/112296#latest-647992))

* 6th Place Solution ([Explanation](https://www.kaggle.com/c/youtube8m-2019/discussion/112403#latest-649376))

* 7th Place Solution ([Explanation](https://www.kaggle.com/c/youtube8m-2019/discussion/112349#latest-648466))

## Semantic Segmentation & Instance Segmentation

#### APTOS 2019 Blindness Detection

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/108065))

* 2nd Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/107926))

* 4th Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/107926#latest-624709))

* 5th Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/107960))

* 7h Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/107987))([code](https://github.com/BloodAxe/Kaggle-2019-Blindness-Detection))

* 8th Place Solution ([Explanation](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/108030))([code](https://github.com/DrHB/APTOS-2019-GOLD-MEDAL-SOLUTION))

* [🏅Gold Medal Solutions list 🏅](https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/108307#latest-623987)

#### iMaterialist (Fashion) 2019 at FGVC6:  

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/discussion/95247#latest-567841))([code](https://github.com/amirassov/kaggle-imaterialist))

* 2nd Place Solution([Explanation](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/discussion/95233#latest-551075))

* 3rd Place Solution([Explanation](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/discussion/95234#latest-555537))

#### TGS Salt Identification Challenge

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/69291#latest-523430))([code](https://github.com/ybabakhin/kaggle_salt_bes_phalanx))

* 4th Place Solution  ([Explanation](https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/69178#latest-527751

))([code](https://github.com/SeuTao/Kaggle_TGS2018_4th_solution))

* 9th Place Solution  ([Explanation](https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/69053#latest-563912))([code](https://github.com/tugstugi/pytorch-saltnet))

#### Airbus Ship Detection Challenge

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/airbus-ship-detection/discussion/74443#latest-456794))

* 6th Place Solution  ([Explanation](https://www.kaggle.com/c/airbus-ship-detection/discussion/71782#latest-558831))

* 8th Place Solution  ([Explanation](https://www.kaggle.com/c/airbus-ship-detection/discussion/71667#latest-558876))([code](https://github.com/SeuTao/Airbus-Ship-Detection-Challenge-2018_8th_place_solution))

* 9th Place Solution  ([Explanation](https://www.kaggle.com/c/airbus-ship-detection/discussion/71595#latest-457550))

* Few lessons learned (4th place) ([Explanation](https://www.kaggle.com/c/airbus-ship-detection/discussion/71667#latest-558876))

#### 2018 Data Science Bowl (DSB2018)

* 1st Place Solution ([Explanation](https://www.kaggle.com/c/data-science-bowl-2018/discussion/54741#latest-477226))([code](https://github.com/selimsef/dsb2018_topcoders/))

* 2nd Place Solution ([Explanation & code](https://github.com/jacobkie/2018DSB))

* 3rd Place Solution ([Explanation](https://www.kaggle.com/c/data-science-bowl-2018/discussion/56393#latest-540344))([code](https://github.com/Lopezurrutia/DSB_2018))

* 4th Place Solution  ([Explanation](https://www.kaggle.com/c/data-science-bowl-2018/discussion/55118#latest-527734))([code](https://github.com/pdima/kaggle_2018_data_science_bowl_solution))

## GAN

#### Generative Dog Images

* P1st lace Solution ([Explanation](https://www.kaggle.com/c/generative-dog-images/discussion/106324))

* Place Solution ([Explanation](https://www.kaggle.com/c/generative-dog-images/discussion/104211))([code](https://www.kaggle.com/yukia18/sub-rals-ac-biggan-with-minibatchstddev))

* Place Solution ([Explanation](https://www.kaggle.com/c/generative-dog-images/discussion/106514))([code](https://www.kaggle.com/lisali/sagan-submit-2?scriptVersionId=18714508))

* Place Solution ([Explanation](https://www.kaggle.com/c/generative-dog-images/discussion/104281))([code](https://github.com/bestfitting/kaggle/tree/master/gandogs))

* Place Solution ([Explanation](https://www.kaggle.com/c/generative-dog-images/discussion/104287))([code](https://www.kaggle.com/dvorobiev/doggies-biggan-sub-data-aug-3))

* [Gold Medal Solutions](https://www.kaggle.com/c/generative-dog-images/discussion/106305)

# 2. DataHack by Analytics Vidya

#### Innoplexus Online Hiring Hackathon: Sentiment Analysis

* 14th Place Solution ([code](https://github.com/pawangeek/Ccmps/tree/master/innoplexus))

* 25th Place Solution ([code](https://github.com/rajat5ranjan/AV-Innoplexus-Online-Hiring-Hackathon-Sentiment-Analysis))

* 27th Place Solution ([code](https://github.com/Laxminarayen/Innoplex_Hackathon))

* 29th Place Solution ([code](https://github.com/chetanambi/Innoplexus-Online-Hiring-Hackathon-Sentiment-Analysis))

#### Genpact Machine Learning Hackathon

* 13th Place Solution ([code](https://github.com/rajat-1994/AV-Genpact-Hackathon))

* 32th Place Solution ([code](https://datahack-prod.s3.amazonaws.com/submissions/genpact-machine-learning-hackathon/440_505172_cf_baseline.ipynb))

#### Game of Deep Learning: Computer Vision Hackathon

* 1st Place Solution ([code](https://github.com/narensahu13/AV-Game-of-Deep-Learning))

* 2nd Place Solution ([code](https://github.com/salilmishra23/AnalyticsVidhya_GameOfDeepLearning))

* 3rd Place Solution ([code](https://www.kaggle.com/tezdhar/avships-densenet-v3))

* 5th Place Solution ([Explanation](https://docs.google.com/document/d/1ULIxyYW2b1zjoYolHI_rgneW8xkw3fVODw6fjqp9VXc/edit#heading=h.u1tdm64ah919))([code](https://github.com/xbassi/game_of_deep_learning))

#### Capillary Machine Learning Hackathon

* 4th Place Solution ([code](https://drive.google.com/file/d/1T2dGWdyCy7gCm5bPbKeZzpmBhKOdlLdx/view?usp=drive_open))

* 12th Place Solution ([code](https://datahack-prod.s3.amazonaws.com/submissions/capillary-machine-learning-hackathon/443_624878_cf_code_eJkEZMW.py))

* 13th Place Solution ([code](https://datahack-prod.s3.amazonaws.com/submissions/capillary-machine-learning-hackathon/443_622728_cf_current_best_y1yhRmZ.py))

# 3. Machine Hack

#### Predicting The Costs Of Used Cars - Hackathon By Imarticus Learning

* 5th Place Solution ([code](https://github.com/chetanambi/Predicting-The-Costs-Of-Used-Cars-Hackathon-By-Imarticus-Learning))

* 11th Place Solution ([Explanation & code](https://www.kaggle.com/rajatranjan/fork-of-mh-predict-cost-of-used-cars-hackathon))

#### Predict A Doctor's Consultation Fee Hackathon

* 3rd Place Solution ([code](https://github.com/chetanambi/Predict-A-Doctors-Consultation-Fee-Hackathon))

* 8th Place Solution ([Explanation & code](https://www.kaggle.com/rajatranjan/predict-consultation-fee-doc-machinehack-re))

#### Predict The Flight Ticket Price Hackathon

* 3rd Place Solution  ([code](https://github.com/chetanambi/Predict-The-Flight-Ticket-Price-Hackathon))

* 11th Place Solution ([Explanation & code](https://www.kaggle.com/rajatranjan/predict-flight-tickets-machine-hack1))

#### Predicting Restaurant Food Cost Hackathon

* 5th Place Solution ([code](https://www.kaggle.com/rajatranjan/machinehack-predict-food-prices))

* 7th Place Solution ([code](https://github.com/chetanambi/Predicting-Restaurant-Food-Cost-Hackathon))

# 4. Driven Data

| Competition

| ---

| [America's Next Top (Statistical) Model](https://github.com/drivendataorg/americas-next-top-statistical-model)

| [Box-Plots for Education](https://github.com/drivendataorg/box-plots-for-education)

| [Countable Care: Modeling Women's Health Care Decisions](https://github.com/drivendataorg/countable-care)

| [From Fog Nets to Neural Nets](https://github.com/drivendataorg/from-fog-nets-to-neural-nets)

| [Keeping it Fresh: Predict Restaurant Inspections](https://github.com/drivendataorg/keeping-it-fresh)

| [Naive Bees Classifier](https://github.com/drivendataorg/naive-bees-classifier)

| [Senior Data Science: Safe Aging with SPHERE](https://github.com/drivendataorg/senior-data-science)

| [Pri-matrix Factorization](https://github.com/drivendataorg/pri-matrix-factorization)

| [Pover-T Tests: Predicting Poverty](https://github.com/drivendataorg/pover-t-tests)

| [Random Walk of the Penguins](https://github.com/drivendataorg/random-walk-of-the-penguins)

| [N+1 Fish, N+2 Fish](https://github.com/drivendataorg/n-plus-one-fish)

| [Power Laws: Forecasting Energy Consumption](https://github.com/drivendataorg/power-laws-forecasting)

| [Power Laws: Anomaly Detection](https://github.com/drivendataorg/power-laws-anomalies)

| [Power Laws: Optimizing Demand-side Strategies](https://github.com/drivendataorg/power-laws-optimization)

| [Power Laws: Cold Start Energy Forecasting](https://github.com/drivendataorg/power-laws-cold-start)

| [Sustainable Industry: Rinse Over Run](https://github.com/drivendataorg/rinse-over-run)

# 5. CrowdANALYTIX

#### Extraction of product attribute values

* 1st Place Solution ([Explanation](https://magicdata.eu/text-mining-machine-learning-detecting-skus/))

#### PKPD Modeling: Predict Exacerbation in patients with COPD

* 3rd Place Solution ([Explanation](https://mlwave.com/how-we-won-3rd-prize-in-crowdanalytix-copd-competition/))

#### Identifying Superheroes from product images

* 34th Place Solution ([code](https://github.com/skyprince999/Identify-Superheroes))

* Another Solution: [code](https://github.com/kanashov/Identifying-Superheroes-from-Product-Images)

* Another Solution: [code](https://github.com/lwkuant/Side_Project_Identifying_Superheroes)

# 6. Hacker Earth

#### Amazon ML Hiring Challenge

* Score: 73.4% Solution ([Explanation & code](https://github.com/anu0012/Amazon-ML-Hiring-Challenge))

* Score 66%: Solution ([Explanation & code](https://github.com/devNaresh/AmazonHiringChallange))