datascience
It consists of examples, assignments discussed in data science course taken at algorithmica.
파일 탐색기
최종 버전 다운로드 (.zip)- commands.txt
- naivebayes1.R
- naivebayes2.R
- decision-trees.R
- linear-regression1.R
- linear-regression2.R
- linear-regression3.R
- logistic-regression.R
- svm2.R
- titanic4-ann.R
- kmeans.R
- datastructures1.R
- datastructures2.R
- datastructures3.R
- eda1.R
- eda2.R
- eda-graphics.R
- ggplot.R
- data-preparation1.R
- data-preparation2.R
- data-preparation3.R
- dplyr.R
- feature-engineering.R
- text.R
- matrices.R
- matrix-tranformations.R
- pca1.R
- pca2.R
- svd.R
- recommendations1.R
- recommendations2.R
- recommendations3.R
- recommendations4.R
- association-rules.R
- Assignment1.pdf
- Assignment2.pdf
- Assignment3.pdf
- sol1.R
- sol2.R
- sol1.R
- Assignment1-Linear Algebra.pdf
- Assignment2-EDA Numeical.pdf
- Assignment3-Statistics.pdf
- Assignment4-Preprocessing.pdf
- Assignment5-Feature Engineering.pdf
- digit-recognizer-knn.R
- test.csv
- train.csv
- badwords.txt
- impermium_verification_labels.csv
- impermium_verification_set.csv
- test.csv
- test_with_solutions.csv
- train.csv
- code.rar
- proposal.pdf
- report.pdf
- slides.pdf
- social-comments.R
- recommendations - 1.R
- recommendations - 2.R
- recommendations - 3.R
- test_v2.csv
- train_v2.csv
- springleaf-v1.R
- titanic1.R
- titanic3.R
- commands.txt
- datastructures1.R
- datastructures2.R
- datastructures3.R
- eda1.R
- eda2.R
- eda-graphics.R
- ggplot.R
- data-preparation-boxcox.R
- data-preparation-dates.R
- data-preparation-handling NA.R
- data-preparation-images.R
- data-preparation-normalization.R
- data-preparation-text.R
- feature-engineering-images.R
- feature-engineering-pca.R
- feature-engineering-text.R
- feature-engineering.R
- matrices.R
- matrix-tranformations.R
- recommendations1.R
- recommendations2.R
- recommendations3.R
- recommendations4.R
- sol1.R
- Assignment1-Linear Algebra.pdf
- Assignment10-Clustering.pdf
- Assignment11-SVM and Neural Networks.pdf
- Assignment12-MLE.pdf
- Assignment2-EDA Numeical.pdf
- Assignment3-Statistics.pdf
- Assignment4-Preprocessing.pdf
- Assignment5-Feature Engineering.pdf
- Assignment6-Probability.pdf
- Assignment7-Distributions.pdf
- Assignment8-Classification.pdf
- Assignment9-Recommendations.pdf
- DegitRecognizer-1.R
- test.csv
- train.csv
- commands.txt
- datastructures1.R
- datastructures2.R
- datastructures3.R
- sampling.R
- string handling in R.R
- date handling.R
- bagged-trees.R
- adaboosted-trees.R
- boosted-trees.R
- decision tree.R
- logistic regression - caret.R
- logistic regression-batchGD.R
- logistic regression-nobias-batchGD.R
- naivebayes-playtennis1.R
- naivebayes-playtennis2.R
- naivebayes-spamclassifier.R
- neuralnetwork.R
- perceptron-batchGD.R
- perceptron-nobias-batchGD.R
- perceptron-stochasticGD.R
- random-forest.R
- svm-batchGD.R
- svm-nobias-batchGD.R
- entropy-gini.R
- logistic_function.R
- probability.R
- item-based-cf.R
- latent-factor-model.R
- user-based-cf.R
- machine learning - parameter grid.R
- supervised ML program - version1.R
- machine learning - evaluation schemes.R
- bivariate-chisquare1.R
- bivariate-chisquare2.R
- bivariate-stats.R
- multivariate-stats-cor-cov.R
- univariate-stats.R
- bivariate-plots.R
- multivariate-plots.R
- univariate-plots.R
- data-preparation- missing data handling1.R
- data-preparation-boxcox.R
- data-preparation-normalization.R
- data-preparation-recoding.R
- supervised ML program - version2.R
- eigenvectors.R
- feature engg-basics.R
- feature-engineering-pca.R
- hand written digit recognizer - ML program.R
- conjugate-gradient-descent.R
- gradient-descent.R
- gradient-naive.R
- steepest-gradient-descent.R
- GBM-modified.R
- GBM.R
- linear-regression1.R
- LR-batchGD.R
- LR-stochasticbatchGD.R
- LR-stochasticGD.R
- multivariate-linear-regression.R
- Assignment1-R basics.pdf
- Assignment2-ML Resampling.pdf
- Assignment3-Univariate Stats.pdf
- Assignment4-Multivariate Stats.pdf
- Assignment5-EDA.pdf
- Assignment6-Linear Algebra.pdf
- Assignment7-Feature Engineering.pdf
- Assignment8-Optimization.pdf
- rcommands.R
- datastructues3.R
- datastructues4.R
- datastructures1.R
- datastructures2.R
- types.R
- digit-recognizer1.R
- digit-recognizer2.R
- 3d-visulizations.R
- gradient-descent-2variables.R
- gradient-descent-onevariable.R
- LR-batchGD.R
- LR-stochasticbatchGD.R
- LR-stochasticGD.R
- titanic-v5.R
- missing data handling-single imputation.R
- 1.revenue-prediction-random.R
- 2.revenue-prediction-cart.R
- 3.revenue-prediction-add-features-rf.R
- 4.revenue-prediction-impute-rf.R
- 5.revenue-prediction-impute-correlation-rf.R
- 6.revenue-prediction-impute-gbm.R
- model-export-filehash.R
- model-export-PMML.R
- model-export-Rdata.R
- model-import-filehash.R
- model-import-Rdata.R
- social-comments-v1.R
- movie recommendation-ubcf.R
- rating prediction-ubcf.R
- recommendations-adhoc-means.R
- recommendations-adhoc-random.R
- recommendations-adhoc1.R
- recommendations-ibcf.R
- recommendations-ubcf.R
- recommendations1.R
- recommendations2.R
- recommendations3.R
- recommendations4.R
- titanic-v1.R
- titanic-v2.R
- titanic-v3.R
- titanic-v4.R
- bivariate-EDA.R
- chisquare-test.R
- cov-cor.R
- multivariate-EDA.R
- revenue-prediction-EDA.R
- univariate-EDA.R
- boosted-tree.R
- cart-c45.R
- formula-vs-grid-model.R
- randomforest-var imp.R
- randomforest.R
- treebag.R
- entropy-gini.R
- probability.R
- model-evaluation.R
- parameter-tuning.R
- titanic-v4.R
- titanic-v5.R
- foreach.R
- parallel-processing-caret.R
- parallel-processing-foreach.R
- feature-creation.R
- zero-var-and-pca.R
- Assignment1-R Basics.pdf
- Assignment2-Univariate Stats.pdf
- Assignment3-Multivariate Stats.pdf
- Assignment4-EDA.pdf
- bagged-trees.R
- evaluation-schemes.R
- random-forest.R
- rcommands.R
- basic-types.R
- datastructues-factor.R
- datastructures-dataframes.R
- datastructures-matrix.R
- datastructures-vector.R
- ensemble-learning.R
- overfitting-underfitting.R
- titanic-v6-tree learning.R
- titanic-v7-bagged tree.R
- titanic-v8-random forest.R
- titanic-v9-nb learning.R
- parallel-processing-caret.R
- data-preparation- missing data handling1.R
- data-preparation-normalization.R
- data-preparation-text.R
- feature-creation.R
- titanic-v10-imputation-rf.R
- titanic-v11-imputation-feature creation-rf.R
- digit-recognizer1.R
- digit-recognizer2.R
- 3d-visulizations.R
- gradient-descent-2variables.R
- gradient-descent-onevariable.R
- 1.revenue-prediction-random.R
- 2.revenue-prediction-cart.R
- 3.revenue-prediction-add-features-rf.R
- 4.revenue-prediction-add-features-gbm.R
- 5.revenue-prediction-impute-rf.R
- 6.revenue-prediction-impute-gbm.R
- titanic-v1-majority-class.R
- titanic-v2-random-predictions.R
- titanic-v3-gender-model.R
- titanic-v4-human discovery.R
- titanic-v5-tree learning.R
- association-rules.R
- topn-recommendations.R
- clustering1.R
- social-comments-v1.R
- eda.R
- model-evaluation1.R
- model-evaluation2.R
- model-export-filehash.R
- model-export-pmml.R
- model-export-Rdata.R
- model-import-predict-filehash.R
- model-import-predict-Rdata.R
- probability.R
- parameter-tuning.R
- entropy-gini.R
- tree-learning.R
- nb-learning.R
- Assignment1-R.pdf
- Assignment2-EDA.pdf
- Assignment3-Model evaluation and selection.pdf
- Assignment4-Probability.pdf
- clustering1.R
- dataframe-container.R
- vector-container.R
- date-processing.R
- r-environment.R
- basic-types.R
- binomial.R
- titanic-v7(tree tuning).R
- ensemble-bagging-math.R
- knn.R
- naive-bayes-learning.R
- dummyvars.R
- missing-data-handling.R
- classification-bagged trees.R
- classification-boosted trees.R
- classification-glm.R
- classification-knn.R
- classification-naive bayes.R
- classification-tree models.R
- regression-bagged models.R
- regression-boosted models.R
- regression-knn.R
- regression-lm-lasso-ridge.R
- regression-rpart.R
- revenue-prediction-random-1.R
- revnue-prediction-knn-2.R
- revnue-prediction-knn-fe-3.R
- correlation-based-filteration.R
- covariance-correlation.R
- creating-new-features.R
- variance-based-filteration.R
- 3d-visulizations.R
- gradient-descent-2variables.R
- gradient-descent-onevariable.R
- titanic-v1(majority).R
- titanic-v2(random).R
- titanic-v3(human discovery1).R
- titanic-v4(human discovery2).R
- titanic-v5(human discovery3).R
- titanic-v6(machine discovery1).R
- LR-batchGD.R
- LR-stochasticbatchGD.R
- LR-stochasticGD.R
- linear-regression1.R
- linear-regression2.R
- linear-regression3.R
- pca.R
- clustering1.R
- clustering2.R
- recommendations-ibcf.R
- recommendations-random.R
- apriori.zip
- association-rules.R
- eda.R
- resampling-cv.R
- resampling-repeated holdout.R
- .Rhistory
- model-selection.R
- model-export-filehash.R
- model-export-pmml.R
- model-export-Rdata.R
- model-import-filehash.R
- model-import-pmml.R
- model-import-Rdata.R
- probability.R
- .RData
- .Rhistory
- entropy-gini.R
- tree-learning.R
- bayes-rule.R
- Assignment1-R.pdf
- Assignment10-Clustering.pdf
- Assignment11-Recommendations.pdf
- Assignment2-EDA.pdf
- Assignment3-Model evaluation and selection.pdf
- Assignment4-Univariate Stats.pdf
- Assignment5-Probability.pdf
- Assignment6-Classification1.pdf
- Assignment7-Classification2.pdf
- Assignment8-Optimization.pdf
- Assignment9-Unsupervised-PCA.pdf
- 1.basic-containers.py
- 10.casting.py
- 2.list.py
- 3.tuples.py
- 4.dictionary.py
- 5.dataframe1.py
- 6.dataframes2.py
- 7.string.py
- 8.functions.py
- 9.paradigms.py
- parameter-tuning.py
- bagging-bt.py
- bagging-et.py
- bagging-math.py
- bagging-rf.py
- rf-capability.py
- boosting-ada.py
- boosting-gbm.py
- decision-tree-capability0.py
- decision-tree-capability1.py
- decision-tree-capability2.py
- decision-tree-capability3.py
- utilities.py
- lazy-knn-scaling.py
- lazy-knn.py
- kernel-svm.py
- linear-svm.py
- logistic-regression.py
- neuralnet.py
- prob-nb.py
- stacking-model.py
- stacking.py
- voting-hard.py
- voting-soft.py
- pca1.py
- pca2.py
- pca3.py
- titanic-tsne.py
- bivariate-eda.py
- multi-variate-eda.py
- univariate-eda.py
- feature-creation.py
- titanic-VIII.py
- regression-knn.py
- regression-lasso.py
- regression-rf.py
- regression-ridge.py
- matrix-eigenvector.py
- outlier-detection.py
- outlier.csv
- clustering-hierarchical.py
- clustering-kmeans1.py
- clustering-kmeans2.py
- kaggle-I.py
- kaggle-II.py
- kaggle-III.py
- kaggle-IV.py
- apriori1.py
- apriori2.py
- dataset.zip
- ML-programming.py
- kaggle-V.py
- model-evalution.py
- model-export.py
- model-serving.py
- data-preparation-one-hot-encoding.py
- kaggle-VI.py
- kaggle-VII.py
- bayes-rule.py
- impurity.py
- probability.py
- Assignment1-predictive analysis.pdf
- Assignment10-clustering.pdf
- Assignment11-recommendations.pdf
- Assignment12-distributions.pdf
- Assignment2-evaluation and tuning.pdf
- Assignment3-probability.pdf
- Assignment4-dt and nb.pdf
- Assignment5-ensemble.pdf
- Assignment6-Optimization1.pdf
- Assignment7-Optimization2.pdf
- Assignment8-knn.pdf
- Assignment9-pca.pdf
- 1.basic-types.py
- 2.list.py
- 3.tuple.py
- 4.dicitonary.py
- 5.data-frames.py
- 6.numpy-array1.py
- 7.numpy-array2.py
- 8.functions.py
- 9.strings.py
- impurity.py
- random.py
- adaboost1.py
- adaboost2.py
- decision-tree.py
- knn1.py
- knn2.py
- logistic-regression.py
- naive-bayes.py
- random-forest1.py
- random-forest2.py
- voting-classifier1.py
- titanic-v4(rf).py
- feature-engg1.py
- titanic-v6(fe,pre,mb).py
- titanic-v7(fe,pre,mb).py
- titanic-v8(fe,knn).py
- pca1.py
- pca2.py
- pca3.py
- decision-trees1.py
- decision-trees2.py
- random-forest-pca.py
- random-forest1.py
- random-forest2.py
- roc1.py
- roc2.py
- recommendations-ibcf.R
- recommendations-random.R
- stacking.py
- titanic-stackedmodels.py
- titanic-voting-classifier.py
- titanic-v1-gender model.py
- feature_importance1.py
- feature_importance2.py
- clustering1.py
- clustering2.py
- clustering3.py
- congress.csv
- dataframes-api1.py
- dataframes-api2.py
- pyspark-test1.x.py
- pyspark-test2.x.py
- rdd-api1.py
- rdd-api2.py
- hour.csv
- ml-pipeline.py
- eda-bivariate.py
- eda-multivariate.py
- eda-univariate.py
- standard-deviation.py
- categorical-variables.py
- model building-ml.py
- model evalution-cv.py
- titanic-v3-decision tree.py
- model-build-export.py
- model-import-predict.py
- parameter-tuning.py
- Assignment1-predictive analysis.pdf
- Assignment2-eda stats.pdf
- Assignment3-eda visual.pdf
- Assignment4-probability.pdf
- Assignment5-Optimization.pdf
- Assignment6-KNN.pdf
- Assignment7-PCA.pdf
- Assignment8-Recommendations.pdf
- 1.basic-types.py
- 2.list.py
- 3.tuple.py
- 4.dictionary.py
- 5.arrays.py
- 6.strings.py
- 7.matrices.py
- bayes-rule.py
- impurity.py
- prob1.py
- random.py
- adaboost-tuning.py
- adaboost.py
- bagged tree-tuning.py
- bagged tree.py
- extreme-trees.py
- gbm-tuning.py
- gbm.py
- random forest-tuning.py
- random forest.py
- stacked-ensemble1.py
- stacking.py
- voting ensemble-tuning.py
- voting ensemble.py
- knn-standardzied-data.py
- knn-tuning.py
- knn.py
- logistic-regression-tuningpy.py
- logistic-regression.py
- naive-bayes.py
- decision tree-tuning.py
- titanic-v6(rf).py
- bivariate-eda.py
- cov-cor.py
- multi variate-eda.py
- univariate-eda.py
- feature-engg1.py
- matrix-eigenvector.py
- pca1.py
- pca2.py
- pca3.py
- pca4.py
- regression_pca.py
- sale_price_prediction1.py
- sale_price_prediction2.py
- cluster-selection.py
- clustering-kmeans.py
- outlier-detection.py
- outlier.csv
- titanic-v1.ipynb
- titanic-v1.py
- titanic-v2.ipynb
- titanic-v2.py
- ML-introduction1.py
- ML-introduction2.py
- ML-introduction3.py
- model-evaluation .py
- model-export .py
- prediction-service.py
- overfit-diagnosis.py
- model-tuning.py
- titanic-v4.py
- titanic-v5.py
- Assignment1-predictive analysis.pdf
- Assignment2-evaluation and tuning.pdf
- Assignment3-probability.pdf
- Assignment4-classification1.pdf
- Assignment5-ensemble1.pdf
- Assignment6-Optimization.pdf
- 1.basic types.py
- 10.rest-service2.py
- 11.programming-styles.py
- 12.array1.py
- 13.arrays2.py
- 2.list.py
- 3.tuple.py
- 4.dataframes1.py
- 5.dataframes2.py
- 6.dictionary.py
- 7.functions.py
- 8.main.py
- 9.rest-service1.py
- nb-text-no-pipeline.py
- nb-text-pipeline.py
- text-processing1.py
- text-processing2.py
- preprocessing-imputing.py
- preprocessing-onehotencoding.py
- preprocessing-scaling.py
- bivariate-eda.py
- multi variate-eda.py
- univariate-eda.py
- titanic-V.py
- titanic-VI.py
- feature-creation.py
- feature-selection1.py
- feature-selection2.py
- feature-selection3.py
- cov-cor.py
- titanic-VII.py
- regression1.py
- regression2.py
- apriori1.py
- apriori2.py
- dataset.zip
- recommenders-knn.py
- recommenders-svd.py
- recommenders-knn.py
- recommenders-svd.py
- titanic-v1-majority-improved.py
- titanic-v1-majority.py
- titanic-v2-gender.py
- titanic-v3-human discovery.py
- pca1.py
- pca2.py
- pca3.py
- clustering-hierarchical.py
- clustering-kmeans1.py
- clustering-kmeans2.py
- roc1.py
- roc2.py
- roc3.py
- firstML.py
- Lifecycle-I.py
- Lifecycle-Deployment.py
- Lifecycle-LocalTest.py
- Lifecycle-ModelServing.py
- titanic-v4-ml.py
- overfit.py
- parameter-tuning-grid.py
- parameter-tuning-random.py
- underfit.py
- impurity.py
- bagged-ensemble1.py
- bagged-ensemble2.py
- boosted-ensemble1.py
- boosted-ensemble2.py
- stacking1.py
- stacking2.py
- knn.py
- gaussian-nb.py
- logistic-regression.py
- Assignment1-predictive analysis.pdf
- Assignment10-pca.pdf
- Assignment2-evaluation and tuning.pdf
- Assignment3-probability.pdf
- Assignment4-dt and nb.pdf
- Assignment5-distributions.pdf
- Assignment6-knn.pdf
- Assignment7-ensemble.pdf
- Assignment8-optimization.pdf
- Assignment9-recommender and association analysis.pdf
- 1-d array.py
- basic-containers.py
- classes.py
- dataframes1.py
- dictionary.py
- functions.py
- list.py
- main1.py
- main2.py
- styles of programming.py
- tuple.py
- regression-ensemble models.py
- regression-knn.py
- regression-linear models.py
- asociation-rules2.py
- association-rules1.py
- dataset.zip
- personalized recommendations-knn-bias.py
- personalized recommendations-knn-no bias.py
- personalized recommendations-objective based-svd.py
- roc-curve-multi class.py
- roc_curve-binary class.py
- feature-reduction1.py
- feature-reduction2.py
- feature-transformation.py
- t-sne1.py
- clustering-hierarchical.py
- clustering-kmeans1.py
- clustering-kmeans2.py
- dummy
- outlier-detection.py
- outlier.csv
- titanic-v1.py
- titanic-v2.py
- titanic-v3.py
- titanic-v4.py
- first-ml.py
- kaggle-ml-I.py
- kaggle-ml-II.py
- 1.stratification.py
- 2.model persistence.py
- 3.model evalution.py
- 4.live-service.py
- 5.live-webservice.py
- parameter tuning-randomized search.py
- parameter-tuning-gridsearch.py
- bagged-ensemble-algorithms.py
- boosted-ensemble-algorithms.py
- stacked-ensemble.py
- voting-ensemble.py
- knn-imputation-scaling.py
- knn-with-scaling.py
- knn-without-scaling.py
- linear-svm.py
- logistic-regression.py
- guassian-nb.py
- split criterion - decision tree.py
- bayes-rule.py
- 1.scaling.py
- 2.imputation.py
- level-matching-solution1.py
- level-matching-solution2.py
- feature-creation.py
- correlation.py
- feature-selection-model driven1.py
- feature-selection-model driven2.py
- feature-selection-stat test driven.py
- kaggle-V-level matching issue.py
- kaggle-V-level matching solved-1.py
- kaggle-V-level matching solved-2.py
- Assignment1-predictive analysis.pdf
- Assignment2-(math)probability.pdf
- Assignment3-(math)distributions.pdf
- Assignment4-naive bayes and knn.pdf
- 1.basic-containers.py
- 10.paradigms.py
- 11.set.py
- 12.comprehension.py
- 13.oop.py
- 2.list-container.py
- 3.tuple.py
- 4.dictionary.py
- 5.dataframes1.py
- 6.dataframes2.py
- 7.arrays1.py
- 8.arrays2.py
- 9.functions.py
- bivariate-eda.py
- multivariate-eda.py
- univariate-plots.py
- 1.feature-scalers.py
- 2.feature-scalers-viz.py
- 3.imputation.py
- 4.category to continuous transformers.py
- 5.categorical features level matching.py
- kaggle-I.py
- roc1.py
- roc2.py
- roc3.py
- recommenders-knn.py
- recommenders-svd.py
- apriori1.py
- apriori2.py
- dataset.zip
- clustering-hierarchical.py
- clustering-kmeans1.py
- clustering-kmeans2.py
- outlier-detection.py
- outlier.csv
- feature-selection1.py
- human-discovery1.py
- human-discovery2.py
- human-discovery3.py
- first-ML-viz.py
- first-ML.py
- model-eval-cv.py
- live-service.py
- model-deploy.py
- kaggle-I(dt-underfit).py
- kaggle-II(dt-overfit).py
- kaggle-III(dt-overfit control).py
- kaggle-IV(nb).py
- kaggle-V(preprocess, fe, rf).py
- kaggle-VI(pr, fe, fi, rf).py
- kaggle-VII(ensemble).py
- parameter-tuning-gridsearch.py
- parameter-tuning-randomsearch.py
- bayes-rule.py
- 1.decision trees.py
- 10.knn-scaled data.py
- 11.logistic-regression.py
- 2.naive bayes.py
- 3.bagged-ensemble.py
- 4.random forest.py
- 5.voting-hard.py
- 6.voting-soft.py
- 7.knn.py
- 8.stacking.py
- 9.adaboost.py
- 1-d arrays.py
- 2-d arrays.py
- dataframes1.py
- dataframes2.py
- imperative-style.py
- list.py
- map.py
- python-imports.py
- python_env.py
- python_imports.py
- series.py
- set.py
- tuple.py
- types.py
- feature-reduction-techniques.py
- lpca1.py
- lpca2.py
- model-deploymentpy.py
- model-serving-web.py
- model-serving.py
- roc-binary.py
- roc-multiclass.py
- recommenders-knn.py
- recommenders-svd.py
- apriori1.py
- apriori2.py
- dataset.zip
- human-discovery-visual-eda.py
- human-discovery1.py
- human-discovery2.py
- human-discovery3.py
- human-discovery4.py
- human-discovery5.py
- machine-learning-introduction.py
- model-evaluation .py
- model tuning-gridsearch.py
- model tuning-randomsearch.py
- level_mismatch_categorical-solution1.py
- level_mismatch_categorical-solution2.py
- level_mismatch_categorical.py
- preprocessing-transformers1.py
- preprocessing-transformers2.py
- feature_creation.py
- feature_selection.py
- overfit vs underfit diagnosis.py
- titanic-dt1.py
- titanic-dt2.py
- bagged-ensemble.py
- boosted-ensemble.py
- voting-ensemble.py
- kaggle-regression1.py
- kaggle-regression2.py
- kaggle-regression3.py
- kaggle-regression4.py
- titanic-knn1.py
- titanic-knn2.py
- bayes.py
- titanic-nb1.py
- titanic-nb2.py
- objective based-linear algorithms.py
- objective based-non linear algorithms.py
- regression-algorithms.py
- Assignment1-predictive analysis.pdf
- Assignment2-Classification1.pdf
- 1d-arrays.py
- 2d-arrays.py
- basic-containers.py
- dataframes1.py
- dataframes2.py
- dictionary.py
- imperative-style.py
- import_script.py
- list.py
- main_in_python.py
- object-oriented-style.py
- python-environment.py
- series.py
- tuple.py
- titanic-final-script1.py
- 1.linear regression-multi variate.py
- 10.robust regression.py
- 11.loss_functions_regression.py
- 2.feature transformation regression.py
- 3.regularized feature transformation regression.py
- 4.kernel-regression.py
- 5.decision tree regression.py
- 6.knn regression.py
- 7.bagged ensemble regression.py
- 8.boosted ensemble regression.py
- 9.target variable transformation.py
- 1.house price prediction(knn).py
- 2.house price prediction(transformed target).py
- 3.house price prediction(combined feature selector and ensemble).py
- 4.house price prediction(combined feature selector and stacked ensemble).py
- 1.outlier detection without ground truth labels.py
- 2.outlier detection with ground truth labels.py
- 1.credit card fraud outliers.py
- credit_card_transactions_data_set_download.txt
- 1. kmeans clustering.py
- 2.hierarchical clustering.py
- 3.spectral clustering.py
- clustering-credit card purchases.py
- credit_card_payments_data_set_download.txt
- 1.scaling-2d.py
- 2.scaling-3d.py
- 3.normalization.py
- association based analysis - 2.py
- association based analysis -1.py
- dataset.zip
- personalized recommendations-knn.py
- personalized recommendations-svd.py
- kde-1d.py
- kde-2d.py
- kde-higher dim.py
- titanic-human discovery(visual-eda).py
- titanic-human discovery1.py
- titanic-human discovery2.py
- titanic-human discovery3.py
- firstML.py
- lifecycle.py
- lifecycle.py
- model evaluation strategies.py
- ada-boosting.py
- bagged-ensembling.py
- boosting classifiers.py
- extra tree-ensembling.py
- rf-ensembling.py
- stacking-classifier.py
- voting classifier.py
- impurity.py
- overfit-control-tree learning.py
- knn-no-scaling.py
- knn-scaling-ohe.py
- knn-scaling-tuning.py
- linear-svm.py
- logistic-regression.py
- naive-bayes.py
- ml-as-a-service.py
- ml-service-flask-client.py
- model-deployment.py
- model-usage.py
- 1.decision tree classification.py
- 2.knn classification.py
- 3.bagged ensemble classification.py
- 4.boosted ensemble classification.py
- 5.logistic regression classification.py
- 6.need of feature transformation.py
- 7.feature transformation classification.py
- 8.kernel svm and kernel logistic regression.py
- 9.loss_functions_classification.py
- feature reduction-lpca.py
- feature reduction-tsne.py
- kl divergence loss-tsne.py
- linear and non-linear feature reduction.py
- titanic-pca.py
- why t distribution-tsne.py
- feature-creation.py
- feature-selection1.py
- feature-selection2.py
- feature-selection3.py
- feature-selection4.py
- classification_utils.py
- clustering_utils.py
- common_utils.py
- kernel_utils.py
- outlier_utils.py
- pca_utils.py
- regression_utils.py
- tsne_utils.py
- 1.scalar-types.py
- 10.object-oriented-style.py
- 11.custom_package.py
- 11.functional-style.py
- 12.import_packages.py
- 13-1 dummy1.py
- 13-2 test_main.py
- 14-1 webservices-python.py
- 14-2 webservice-client.py
- 2.list.py
- 3.tuple.py
- 4.dictionary.py
- 5.dataframes1.py
- 6.data frames2.py
- 7.1-d array.py
- 8.2-d array.py
- 9.imperative-style.py
- linear pca.py
- manifold learning algorithms.py
- 1.density estimators comparison - 1d.py
- 2.density estimators comparison - 2d.py
- 3.density estimators comparison - higher dim.py
- bivariate-eda.py
- multivariate-eda.py
- univariate-plots.py
- 1.regression-pattern.py
- 2.linear models.py
- 3.linear models-feature transfomration.py
- 4.linear models-kernel.py
- 5.dt and knn.py
- 6.ensemble.py
- 7.robust regression.py
- 1.house price prediction.py
- 2.house price prediction.py
- 3.house price prediction.py
- 4.house price prediction.py
- 5.house price prediction.py
- 6.house price prediction-automl-parameter tuning.py
- 7.house price prediction-automl-full pipeline.py
- hard-classification.py
- imbalanced-classification.py
- soft-classification.py
- 1.clustering-pattern.py
- 2.clustering-kmeans.py
- 3.kmeans-limitations.py
- 4.clustering-hierarchical.py
- 5.clustering-gmm.py
- 1.classification_pattern.py
- 10.linear models - kernels.py
- 11.prob learning.py
- 2.knn learning.py
- 3.decision tree learning.py
- 3.random-forest.py
- 4.bagged-ensemble.py
- 5.boosted-ensemble.py
- 6.voting-ensemble.py
- 8.linear-models.py
- 9.linear models - feature transformation.py
- ovr-models.py
- model evaluation-test data driven.py
- model evaluation-validation data driven.py
- parameter search-solutions.py
- overfit-underfit-dt.py
- overfit-underfit-knn.py
- overfit-underfit-linear models1.py
- overfit-underfit-linear models2.py
- correlation.py
- level-mismatch-problem.py
- pipelines.py
- scaling.py
- titanic_solution-automl1(hyper parameter tuning).py
- titanic_solution-automl2(full pipeline tuning).py
- titanic_solution1(knn).py
- titanic_solution2(kernel svm).py
- titanic_solution31(average ensemble).py
- titanic_solution32(voting ensemble).py
- titanic_solution33(stacked ensemble).py
- titanic_solution4(kernel svm with feature engg).py
- feature selection-embedded.py
- feature selection-statistical.py
- feature selection-wrapper.py
- 1.build-titanic-model.py
- 2.titanic-model-service.py
- 3.titanic-webclient.py
- classification_utils.py
- common_utils.py
- kernel_utils.py
- outlier_utils.py
- regression_utils.py
- 1.basic types.py
- 10.arrays-2d.py
- 2.list.py
- 3.tuple.py
- 4.dictionary.py
- 5.data frames1.py
- 6.dataframes2.py
- 7.imperative style.py
- 8.object oriented style.py
- 9.arrays-1d.py
- dont overfit-pipeline1.py
- dont overfit-pipeline2.py
- dont overfit-pipeline3.py
- dont overfit-pipeline4.py
- dont overfit-pipeline5.py
- dont overfit-pipeline6.py
- feature selection.py
- 1.regression pattern.py
- linear models-regression.py
- non linear models1 - regression.py
- non linear models2 - regression.py
- robust regression.py
- 1-1.house price.py
- 1-2.house price.py
- 2.house price.py
- 3.house price.py
- 4.house price prediction.py
- 5.house price prediction.py
- 6.house price prediction.py
- 7.house price prediction- automl.py
- dimensionality reduction-pca1.py
- dimensionality reduction-pca2.py
- dimensionality reduction-tsne1.py
- dimensionality reduction-tsne2.py
- 1.clustering-pattern.py
- 2.clustering-kmeans.py
- 3.kmeans-limitations.py
- 4.clustering-hierarchical.py
- 5.clustering-gmm.py
- association based recommedations1.py
- association based recommedations2.py
- movie rating prediction-knn1.py
- movie rating prediction-knn2.py
- movie rating prediction-svd.py
- timeseries-ar(gridsearch).py
- timeseries-arima(gridsearch).py
- timeseries-arma(gridsearch).py
- timeseries-cross validation.py
- timeseries-model building and evaluation.py
- timeseries-preprocessing.py
- timeseries-properties.py
- anova test.py
- chisquare test.py
- t test.py
- dummy classification1.py
- dummy classification2.py
- dummy classification3.py
- outlier detection.py
- outliers.py
- house price model building and deployment.py
- webclient.py
- machine learning introduction1.py
- machine learning introduction2.py
- machine learning introduction3.py
- model evaluation1.py
- model evaluation2.py
- model evaluation3.py
- auto ml1.py
- automl2.py
- titanic-v1.py
- titanic-v2.py
- titanic-v3.py
- titanic-v4.py
- titanic-v41(knn issues).py
- titanic-v42(knn).py
- titanic-v5.py
- titanic-v6.py
- titanic-v7.py
- decision tree - overfit and underfit.py
- overfit-underfit-objective based learning.py
- bivariate-eda.py
- multivariate-eda.py
- univariate-eda.py
- feature transformation.py
- linear-models.py
- naive bayes.py
- non linear models-1.py
- non linear models-2.py
- non linear models-3.py
- pipelines.py
- classification metrics-hard.py
- classification metrics-soft.py
- classification_utils.py
- clustering_utils.py
- common_utils.py
- kernel_utils.py
- linear_algebra_utils.py
- outlier_utils.py
- pca_utils.py
- regression_utils.py
- tsne_utils.py
- .Rhistory
- customer-satisfaction1.R
- .gitattributes
- .gitignore
- README.md
// repository documentation
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