├── .gitattributes ├── 9781484235638.jpg ├── CNN ├── Code files │ ├── CNN simple example.ipynb │ ├── CNN simple example.xlsx │ ├── Improvement using CNN (1).ipynb │ ├── Issue+with+traditional+NN.ipynb │ └── kerasr_cnn_code.r ├── ~$CNN v2.docx ├── ~$CNN working details.xlsx ├── ~$CNN.docx ├── ~$ural network.docx ├── ~WRL0003.tmp ├── ~WRL1041.tmp ├── ~WRL1167.tmp └── ~WRL1848.tmp ├── Clustering ├── code files │ ├── clustering output interpretation.xlsx │ ├── clustering process.xlsx │ ├── clustering.ipynb │ └── clustering_code.R ├── ~$clustering output interpretation.xlsx ├── ~$clustering process.xlsx ├── ~$clustering.pptx ├── ~$ustering.docx └── ~WRL0003.tmp ├── Contributing.md ├── Decision tree ├── excel files │ ├── Decision tree.R │ ├── Decision tree.ipynb │ ├── categorical dependent continuous independent variable.xlsx │ ├── categorical dependent multiple continuous independent variable.xlsx │ ├── continuous variable dependent and independent variables.xlsx │ ├── continuous variable dependent and independent variables2.xlsx │ ├── decision tree categorical dependent and independent variables.xlsx │ ├── ~$continuous variable dependent and independent variables.xlsx │ └── ~$continuous variable dependent and independent variables2.xlsx ├── ~$cision tree v2.docx ├── ~$cision tree.docx ├── ~$decision_tree continuous variable2.xlsx ├── ~$dt_example.xlsx ├── ~$emp_sal_detail_Decision Tree.xlsx ├── ~WRL0003.tmp ├── ~WRL0004.tmp └── ~WRL3830.tmp ├── GBM ├── GBM.R ├── GBM.ipynb ├── adaboost.xlsx ├── ~$adaboost.xlsx └── ~$gbm.docx ├── LICENSE.txt ├── Linear regression ├── Baby age to weight relation │ └── Baby age to weight relation.xlsx ├── Linear regression Python code.ipynb ├── Multiple linear regression │ └── linear_multi_reg_example.csv ├── Solving a simple linear regression │ ├── Multivariate linear regression.R │ ├── issues with correlated independent variables.R │ ├── linear regression 101.xlsx │ └── simple linear regression.R ├── linear_multi_reg_example.csv ├── linear_reg_example.csv ├── multi_linear_reg2.csv └── ~$near regression.docx ├── Logistic regression ├── Credit default prediction.ipynb ├── Python logistic regression.ipynb ├── auc metric graphs.xlsx ├── credit_training.csv ├── iris sample │ ├── iris sample estimation.xlsx │ ├── iris sample.xlsx │ └── ~$iris sample estimation.xlsx ├── iris_sample.csv ├── logisitic regression.R ├── ~$auc metric graphs.xlsx ├── ~$gistic regression1.docx └── ~WRL0392.tmp ├── Neural network ├── code files │ ├── Gradient descent batch size.xlsx │ ├── NN .xlsx │ ├── NN.r │ └── Neural+network.ipynb ├── ~$NN v2.docx ├── ~$NN v2.xlsx ├── ~$NN.docx ├── ~$NN.xlsx ├── ~$scaling.xlsx └── ~WRL3616.tmp ├── PCA ├── PCA_2vars │ ├── PCA R.R │ ├── PCA.ipynb │ ├── PCA_2vars.xlsx │ ├── data.csv │ ├── pca mnist.R │ ├── pca_3vars.xlsx │ ├── pca_data.xlsx │ ├── pca_r.csv │ ├── train.csv │ └── ~$PCA_2vars.xlsx ├── ~$pca.docx ├── ~$pca_3vars.xlsx └── ~$pca_data.xlsx ├── README.md ├── RNN ├── Code files │ ├── RNN new version.xlsx │ ├── RNN text generation.ipynb │ ├── airline_sentiment.csv │ ├── alice.txt │ ├── kerasR_code_RNN.r │ ├── lstm working details.xlsx │ ├── simple RNN working verification.xlsx │ ├── simpleRNN basics.ipynb │ └── ~$lstm working details.xlsx ├── ~$RNN - Copy.xlsx ├── ~$RNN new version.xlsx ├── ~$RNN v2.docx ├── ~$RNN v3.docx ├── ~$RNN working verification.xlsx ├── ~$RNN.docx ├── ~$RNN.xlsx ├── ~$RNN101 - Copy.xlsx ├── ~$RNN101.xlsx ├── ~$Rnn new.xlsm ├── ~WRL0003.tmp ├── ~WRL0004.tmp ├── ~WRL0858.tmp ├── ~WRL1945.tmp ├── ~WRL3253.tmp └── ~WRL3837.tmp ├── Random forest ├── Files │ ├── meandecreasegini.xlsx │ ├── random forest.ipynb │ ├── rf_code.R │ ├── titanic_train.csv │ ├── train_sample.csv │ └── variable importance and parameter tuning.R └── ~$meandecreasegini.xlsx ├── Recommender systems ├── code files │ ├── Matrix+factorization.ipynb │ ├── UBCF.R │ ├── UBCF.ipynb │ ├── matrix factorization example.xlsx │ ├── matrix factorization.R │ ├── movie_rating.csv │ └── ubcf.xlsx 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