文件名称：KaggleStruggle：Kaggle奋斗
文件大小：119.31MB
文件格式：ZIP
更新时间：2024-02-24 00:08:19
python machine-learning scikit-learn pandas seaborn
Kaggle奋斗 卡格格奋斗
【文件预览】：
KaggleStruggle-master
----movie_reviews()
--------movie-review-Scikit-Learn-Tutorial.ipynb(20KB)
----kaggle-api.ipynb(31KB)
----mechanisms-of-action()
--------eda.ipynb(15.32MB)
----nyc-taxi-trip-duration()
--------nyc-taxi-trip-eda.ipynb(583KB)
----loan-approval()
--------loan-approval-eda.ipynb(3.91MB)
----mpg()
--------eda-plotly.ipynb(3.11MB)
--------eda.ipynb(1.32MB)
----breast-cancer()
--------sklearn_breast_cancer_dataset.ipynb(187KB)
--------breast-canser-tensorflow2.ipynb(11KB)
--------eda.ipynb(733KB)
----so-survey-2017()
--------so-survey-2017-eda.ipynb(765KB)
----hackerrank-survey-2018()
--------hackerrank-survey-2018-eda.ipynb(369KB)
----pokemon()
--------pokemon_networkx.ipynb(1.67MB)
--------pokemon_plotnine.ipynb(2.05MB)
----instacart()
--------instacart-exploring-data.ipynb(1.58MB)
----bike-sharing-demand()
--------data()
--------bike-sharing-demand-casual-registered.ipynb(118KB)
--------bike-sharing-demand-ensemble-model.ipynb(130KB)
--------bike-sharing-demand-EDA.ipynb(712KB)
--------bike-sharing-demand-rf.ipynb(130KB)
----LICENSE(1KB)
----spooky-author-identification()
--------data()
--------spooky_NLP_EDA.ipynb(1.58MB)
----predict-future-sales()
--------eda.ipynb(221KB)
----titanic()
--------03_DecisionTree.ipynb(72KB)
--------titanic-tf-neural-network.ipynb(83KB)
--------06-one-hot-encoding.ipynb(46KB)
--------titanic_for_colab.ipynb(12KB)
--------titanic-tf-basic-for-colab.ipynb(37KB)
--------titanic-xgb.ipynb(43KB)
--------04-preprocessing-missing-value.ipynb(168KB)
--------preprocessing.ipynb(86KB)
--------05-preprocessing-binning.ipynb(14KB)
--------02_plotnine.ipynb(854KB)
--------01_pandas_basic.ipynb(45KB)
----talkingdata-adtracking-fraud-detection()
--------ad_fraud_detection_eda.ipynb(783KB)
--------xgboost.ipynb(4KB)
----kaggle-survey-2020()
--------eda.ipynb(5.3MB)
----bank-marketing()
--------classification.ipynb(59KB)
--------eda.ipynb(1.55MB)
----historical-sales()
--------eda.ipynb(23KB)
----so-tag-network()
--------python-networkx-analysis.ipynb(283KB)
----air-passengers()
--------time-series-analysis.ipynb(149KB)
----house-prices()
--------03-regression.ipynb(38KB)
--------02-eda.ipynb(2.8MB)
--------01-data-exploration.ipynb(609KB)
--------house-price-rf.ipynb(1.24MB)
----.gitignore(75B)
----word2vec-nlp-tutorial()
--------tutorial-part-4.ipynb(36KB)
--------MLWave-online-learning-perceptron.ipynb(36KB)
--------data()
--------tutorial-part-1.ipynb(757KB)
--------tutorial-part-3-4.ipynb(27KB)
--------tutorial-part-5.ipynb(29KB)
--------300features_40minwords_10text(42.04MB)
--------KaggleWord2VecUtility.py(3KB)
--------tutorial-part-2.ipynb(320KB)
----sf-crime()
--------sf-crime-vec-sklearn.ipynb(38KB)
----pima-indians-diabetes()
--------baseline.ipynb(53KB)
--------ml-tree.ipynb(58KB)
--------baseline-tensorflow2-regression.ipynb(113KB)
--------eda.ipynb(1.53MB)
--------baseline-tensorflow2.ipynb(64KB)
----jigsaw-toxic-comment-classification-challenge()
--------eda.ipynb(295KB)
----insurance-cross-sell()
--------classification.ipynb(15KB)
--------eda.ipynb(177KB)
----data-science-bowl-2018()
--------imaging-newbies.ipynb(296KB)
----wine-reviews()
--------plotnine.ipynb(1.05MB)
----telco-customer-churn()
--------telco_prediction_03_grid_search.ipynb(310KB)
--------telco_eda.ipynb(2.57MB)
--------telco_prediction_04_random_search.ipynb(310KB)
--------telco_prediction_03.ipynb(162KB)
--------telco_prediction_02.ipynb(246KB)
--------telco_prediction_01.ipynb(370KB)
----README.md(33B)
----iris()
--------iris-eda.ipynb(1.15MB)
----kaggle-survey-2017()
--------data()
--------Kaggle-ML-DS-survey-2017-EDA-FAQ.ipynb(2.11MB)
----hotel-booking-demand()
--------ml.ipynb(14KB)
--------eda.ipynb(3.89MB)