TECHNOLOGY-: Statistical Models: Linear Regression ,Random Forest, Decision tree etc. Ensemble Models: Bagging, Boosting etc. Deep Learning: RNN, LSTM , CNN , Single Layer Perceptron, Multi-layer perceptron , Encoder-decoder, FFN Classification-Models: Logistic, Random Forest, Ada-boost, GBM, Light GBM, XGBoost etc. Descriptive-Analysis: Power BI. Clustering: - K-Mean, Hierarchical,K++Mean, DB-Scan, HDB-scan, Graph Based clustering, Fuzzy Clustering . Graph Chart: Box-Plot, Bar-graph, Histogram, Pie chart, Scatter Plot. Variable -selection: PCA, Factor Analysis and Different Statistical Models. Regression-Statistics: R-square, RMSE. Accuracy Test: ROC, Confusion Matrix, F-score, Precision, Recall, Accuracy. Correlation Test :- Pearson, Spearman, T-test,and Chi Square., Measure of variance: ANOVA, T test, Ztest, , Chi Square test Rest Api – Flask ,Aws