├── AB_Testing ├── README.MD ├── Statistical Sampling │ ├── Cluster_Sampling.py │ ├── README.MD │ ├── Stratified Sampling.py │ ├── Systematic_Sampling.py │ └── Weighted Sampling.py └── Statistical tests for AB_testing │ ├── 2 sample Z Test Porportions.py │ ├── 2-Sample-2-sided-T-test.py │ ├── 2-Sample-2-sided-Z-test.py │ ├── Chi2-Test.py │ ├── Difference_in_Means_and_in_Medians.py │ └── README.MD ├── Anomaly_detection_techniques ├── Anomaly Detection Paper.pdf ├── README.MD ├── Unsupervised_Learning_Anomaly_Detection.ipynb └── data.csv ├── LDA-LFM-Recommender-System ├── ConferencePresentation_SAC2021.pdf ├── Figures │ ├── 10_30_rating_review_figure.png │ ├── LDA_figure.png │ ├── Matrix_Factorization_figure.png │ ├── Picture 2.png │ ├── Picture 3.png │ └── Results 1.png ├── Pyton-Code │ ├── LDA-LFM: Baseline Recommender System.py │ ├── LDA-LFM: Data Preparation.py │ ├── LDA-LFM: Data Transformation NLP.py │ ├── LDA-LFM: Data Transformation.py │ ├── LDA-LFM: LDAFirst Recommender System.py │ ├── LDA-LFM: Offset Recommender System.py │ ├── LDA-LFM: Proposed LDA-LFM algorithm.py │ ├── LDA-LFM: Required Libraries.py │ ├── LDA-LFM: Sparse Matrix Generation.py │ └── LDA-LFM: Standard Matrix Factorization Recommender System.py └── README.MD ├── README.md ├── RFM Special Marketing Segmentation ├── README.MD ├── RFM_Segmentation.pdf └── Recency_Frequency_Monetary_model.R ├── Recommender Systems ├── README.MD ├── TopN_MovieRecommender.pdf └── TopN_MovieRecommender.py ├── Statistical-tests ├── 2 sample Z Test Porportions.py ├── 2-Sample-2-sided-T-test.py ├── 2-Sample-2-sided-Z-test.py ├── Binomial_2sample_2sided.png ├── Chi2-Test.py ├── Chi2_Test.png ├── Chi2_test_checking_fairness_of_dice.py ├── Difference_in_Means_and_in_Medians.py ├── Mann_Whitney_U_test_large_sample.py ├── Mann_Whitney_U_test_small_sample.py ├── README.MD ├── WilcoxonMannWhithney_2sample.py ├── Z-test.png └── Z-test.py ├── Supervised Learning Algorithms ├── AdaBoost │ ├── AdaBoost.png │ ├── AdaBoost.py │ └── README.md ├── Bagging │ ├── Bagging.png │ ├── Bagging.py │ └── README.md ├── Decision Trees │ ├── DecisionTree.png │ ├── Decision_Tree_Clustering.R │ ├── Decision_Trees_Clustering.pdf │ └── README.MD ├── DecisionTrees │ ├── DecisionTrees.png │ ├── DecisionTrees.py │ ├── DecisionTrees2.png │ └── README.MD ├── Grab The Secrets To A Six-Figure Data Science Career!... FOR FREE (11).png ├── GradientBoostingModel │ ├── GBM.png │ ├── GBM.py │ └── README.md ├── Linear Discriminant Analysis (LDA) │ ├── LDA.R │ ├── LDA.png │ ├── LDA.py │ └── README.MD ├── Linear Regression │ ├── LinearRegression.png │ ├── LinearRegression.py │ ├── README.MD │ └── Readme.md ├── Logistic Regression │ ├── LogisticRegression.png │ ├── LogisticRegression.py │ └── README.MD ├── Naive Bayes │ ├── NaiveBayes.png │ ├── NaiveBayes.py │ └── README.MD ├── README.MD ├── Random Forest │ ├── README.md │ ├── Random Forest.png │ ├── RandomForest.png │ └── RandomForest.py └── XGBoost │ ├── README.md │ ├── XGBoost.png │ ├── XGBoost.py │ ├── XGBoost2.png │ └── XGBoost_model.scala ├── Time-Series Seasonality Differencing ├── README.md └── Time Series Differencing.py └── Unsupervised Learning Algorithms ├── Hierarchical Clustering ├── Dendogram.png ├── HC.png ├── HirarchicalClustering.py ├── README.md └── RawData.png ├── K-Means ├── Inertia.png ├── K-Means.png ├── K-means example.png ├── 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