├── 1.LinearRegressionTillCloud ├── Admission_Prediction.csv ├── Advertising.csv ├── GradientDescent.gif ├── L1.PNG ├── Linear Regression.ipynb ├── LinearGraph.png ├── MLApplicationFlow.jpg ├── MLApplicationFlow_bold.png ├── MLApplicationFlow_col.png ├── PolynomialGraph.png ├── Position_Salaries.csv ├── Procfile ├── RSS.PNG ├── RSS_reg.PNG ├── RSquared.png ├── TSS.PNG ├── TSS2.PNG ├── TrainingPipeline.jpg ├── adjr.PNG ├── adjr2.PNG ├── app.yaml ├── cor.PNG ├── cre_pro.PNG ├── dashboard.PNG ├── elasticNet.PNG ├── fileForDoc.py ├── final_snap.PNG ├── finalized_model.pickle ├── flask_flow.PNG ├── folder_structure.PNG ├── generalEquation.PNG ├── iam.PNG ├── main.py ├── matrix1.PNG ├── minima.PNG ├── minima2.png ├── multicollinearity_flow.PNG ├── multicollinearity_pic.PNG ├── new_m.PNG ├── partialDerivatives.PNG ├── region_select.PNG ├── requirements.txt ├── residual.png ├── ridge.PNG ├── ridge_vs_lasso.PNG ├── select_project.PNG ├── setup.PNG ├── static │ └── css │ │ ├── main.css │ │ └── style.css ├── sumOfResiduals.PNG ├── templates │ ├── base.html │ ├── index.html │ └── results.html ├── testingPipeline.jpg ├── testing_pipeline.PNG └── tutorial.PNG ├── 12.SVM ├── Admission_Prediction.csv ├── FlaskApp.PNG ├── Kernels.png ├── ProjectStruct.PNG ├── SVM.docx ├── SVM_3D_Hyperplane.png ├── SVM_hyperplane.png ├── SVM_intution.png ├── SVR.png ├── SupportVectorMachines.ipynb ├── TEST.pickle ├── Untitled.ipynb ├── __pycache__ │ ├── clientApp.cpython-36.pyc │ └── clientApp.cpython-37.pyc ├── application.py ├── clientApp.py ├── createAPP.PNG ├── finalResult.PNG ├── finalized_model.pickle ├── hyperplane.png ├── red-wine-quality-cortez-et-al-2009.zip ├── requirements.txt ├── static │ └── css │ │ ├── main.css │ │ └── style.css ├── templates │ ├── base.html │ ├── index.html │ └── results.html ├── test1.joblib ├── test1.pickle ├── webApp.PNG ├── winequality-red.csv └── zipCreation.PNG ├── 2.Logistic-regression_final ├── AUC.PNG ├── Logistic Regression.ipynb ├── MLApplicationFlow_bold.png ├── Procfile ├── ROC.PNG ├── Social_Network_Ads.csv ├── app.py ├── cf1.PNG ├── cf2.PNG ├── confusionMatrix.PNG ├── derivative.PNG ├── diabetes.csv ├── gitignore ├── logisticRegression.PNG ├── logistic_deploy.py ├── logistic_function.PNG ├── logistic_regression.py ├── logit.PNG ├── manip1.PNG ├── modelForPrediction.sav ├── multi.PNG ├── old │ └── Logistic_Regression_Mohit.ipynb ├── prediction.PNG ├── projectStructure.PNG ├── requirements.txt ├── sandardScalar.sav ├── sigmoid.PNG ├── tandardScalar.sav ├── testing_pipeline.PNG └── tradeoff.PNG ├── 3.Decision tree ├── DecisionTree_DEPLOYMENT │ ├── Procfile │ ├── app.py │ ├── modelForPrediction.sav │ ├── pca_model.sav │ ├── requirements.txt │ └── standardScalar.sav ├── Decision_tree-deploy.png ├── Decision_tree.ipynb ├── Decision_tree1.png ├── MLApplicationFlow.png ├── Source.gv ├── app.py ├── class_distribution.csv ├── cv1.png ├── cv2.png ├── cv3.png ├── cv4.png ├── data_class.PNG ├── entropy.png ├── entropy1.png ├── entropy_tree.png ├── example │ ├── 1.png │ ├── 10.1.PNG │ ├── 10.2.PNG │ ├── 11.PNG │ ├── 12.PNG │ ├── 13.PNG │ ├── 14.PNG │ ├── 15.PNG │ ├── 16.PNG │ ├── 2.png │ ├── 3.1.PNG │ ├── 3.PNG │ ├── 4.PNG │ ├── 5.PNG │ ├── 6.PNG │ ├── 7.PNG │ ├── 8.PNG │ ├── 9.PNG │ ├── entropyVsgini.PNG │ ├── formula_entropy.PNG │ ├── gini.PNG │ └── inform_gain.PNG ├── formula1.png ├── formula2.png ├── formula3.png ├── ginni.png ├── hypr_params.PNG ├── info_gain.png ├── modelForPrediction.sav ├── mytree.dot ├── mytree.png ├── pca_model.sav ├── standardScalar.sav ├── testing_pipeline.png ├── tree_example.png ├── tree_explain.PNG └── winequality_red.csv ├── 4.EnsembleLearning_And_RandomForest ├── 1.png ├── 2.png ├── 3.PNG ├── 6.PNG ├── 7.PNG ├── 8.png ├── Ensemble.png ├── EnsembleLearningAndRandomForest.ipynb ├── MLApplicationFlow.png ├── cv1.png ├── cv2.png ├── cv3.png ├── cv4.png ├── dep1.png ├── dep2.png ├── dep3.png ├── dep4.png ├── dep5.png ├── dep6.png ├── diabetes.csv └── modelForPrediction.sav ├── 5.KNN ├── 1.png ├── 10.png ├── 11.png ├── 12.png ├── 13.png ├── 14.png ├── 15.png ├── 2.png ├── 3.png ├── 4.png ├── 5.png ├── 6.png ├── 7.png ├── 8.png ├── 9.png ├── KNN_CrossValidation.ipynb ├── MLApplicationFlow.png ├── app.py ├── cv1.png ├── cv2.png ├── cv3.png ├── cv4.png ├── deploy │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png ├── diabetes.csv ├── kNN_deployment │ ├── app.py │ ├── diabetes.csv │ ├── knn.py │ ├── modelForPrediction.sav │ └── standardScalar.sav ├── modelForPrediction.sav ├── standardScalar.sav └── testing_pipeline.png ├── 6.1.Link For Boosting Class.txt ├── 6.Ensemble_boosting ├── 6.1.Link For Boosting Class.txt ├── Boosting.ipynb ├── MLApplicationFlow_bold.png ├── app.yaml ├── app_yaml.PNG ├── available_projects.PNG ├── average_residual_tree.PNG ├── ba1.PNG ├── boosting_basic.PNG ├── bw1.PNG ├── cp1.PNG ├── dashboard.PNG ├── disable.PNG ├── final.PNG ├── get_started.PNG ├── heart_disease.PNG ├── heart_disease.csv ├── hessian.PNG ├── main.py ├── manage_resources.PNG ├── normalized_wt.PNG ├── nsw1.PNG ├── pima-indians-diabetes.csv ├── project_structure.PNG ├── pseudo_residuals.PNG ├── pseudo_residuals1.png ├── region_selection.png ├── requirements.txt ├── residual_tree.PNG ├── residual_tree1.png ├── scaler_model.pickle ├── sw1.PNG ├── testing_pipeline.PNG ├── weights.csv ├── xgboost_model.pickle └── yes.PNG ├── 7.Clustering ├── Assignment │ └── clusteringAssignment.ipynb ├── Clustering.ipynb ├── Mall_Customers.csv ├── Untitled.ipynb ├── after_deployment.png ├── app.py ├── bad cluster.PNG ├── conf1.PNG ├── cred.PNG ├── elbow.PNG ├── final.PNG ├── final_ck.PNG ├── finalized_model.pickle ├── folder_structure.png ├── good_cluster.PNG ├── kudo.PNG ├── local_git.PNG ├── minimize_ck.PNG ├── non_overlapping.PNG ├── point_def.PNG ├── prec_recall1.PNG ├── prec_recall2.PNG ├── prob_conf.png ├── requirements.txt ├── sil.PNG ├── sil2.PNG ├── testing_pipeline.PNG ├── unsupervised_ApplicationFlow.png ├── wck_formula.PNG ├── web_app.PNG └── web_app2.PNG ├── 8.PCA ├── 8.1 PCA.txt ├── PCA.ipynb ├── PCA_intro.png ├── PCA_intro1.png ├── axis_rotate.PNG ├── best_fit.PNG ├── covariance.PNG ├── fig_code │ ├── ML_flow_chart.py │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── data.cpython-37.pyc │ │ ├── figures.cpython-37.pyc │ │ ├── helpers.cpython-37.pyc │ │ ├── linear_regression.cpython-37.pyc │ │ └── sgd_separator.cpython-37.pyc │ ├── data.py │ ├── figures.py │ ├── helpers.py │ ├── linear_regression.py │ ├── sgd_separator.py │ └── svm_gui.py ├── glass.data ├── glass.names ├── intro.png ├── move_average.PNG ├── points.PNG ├── projected_points.PNG ├── scree.PNG └── table.PNG ├── 9.Naive_Bayes ├── Assignment │ └── Naive_Bayes.ipynb ├── MLApplicationFlow.png ├── Naive Bayes.ipynb ├── __pycache__ │ └── nb_deploy.cpython-37.pyc ├── app_creation.PNG ├── application.py ├── aws_buildapp.PNG ├── diabetes.csv ├── fig1.PNG ├── fig2.PNG ├── fig3.PNG ├── fig4.PNG ├── final_result.PNG ├── 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