✍🏻 The data science lab

The repository is contained the several models as well as models tutorial. There are various kinds of works related such as:

• Time series model: It covers statistical analytics on time series data.
• Statistic analysis / technique: This provides useful / helpful statistical tasks that could be integrate in the models.
• Libraries tutorial: The useful python libraries provides as the tutorials, which is effortless to follow.
• Deployment: The branch to keep materials for ML Model deployment.
• Computer vision: It contained both of notebook file and branch realted to computer vision topic.
• Market risk: This is not my main area, which is credit risk. Thus, all related to market risks are placed under this topic.
• Customer and Marketing: Recently, it is better to know how to apply data science skills with marketing areas.
• PySpark: -soon-
• Natural language processing (NLP): -soon-
• Others: -soon-

There will be many more to come in the future.

Time series model

• bayesian_linear_regression.ipynb: The Bayesian linear regression is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference.
• ARIMAModel.ipynb: The ARIMA Model (Autoregressive Integrated Moving Average) used for stock price prediction.
• SARIMAModel.ipynb: The SARIMA Model (Seasonal Autoregressive Integrated Moving Average) used for oil price prediction.
• pca.ipynb: The Principal Component Analysis (PCA) appiled for time series data.
• pcr.ipynb: The Principal Component Analysis (PCA) with linear regression appiled for time series data.
• pls_regression.ipynb: The Partial Least Squares (PLS) for time series data.
• timeSeriesSlide.ipynb: The time series model cross validation with time slide window.
• timeSeriesSplit.ipynb: The time series model cross validation with time split window.
• timeSplit.sas: Utilised SAS to perform the time series model cross validation with time split window.

Statistic analysis / technique

• MICE.ipynb: MICE is the Multivariate Imputation by Chained Equations.
• SHAPInterpreter.ipynb: SHAP values are used to explain individual predictions made by a model.
• chi_squareTest.ipynb: The Chi-square test for categorical data.
• k-fold.sas: Utilised SAS to perform K-Fold cross validation.
• one_hot_encoding.ipynb: The transformation categorical data for modelling purpose.
• 1_WayANOVA.ipynb: The 1-Way ANOVA Statistical testing for categorical features with regression problem.
• Theil_SenRegression.ipynb: The Theil-Sen Regression for outlier data.

Libraries tutorial

• PyCaretModel.ipynb: PyCaret is an open source, low-code machine learning library.
• optimumBinning.ipynb: The tutorial for using OptBinning library to develop credit score card.
• pipelineModel.ipynb: The tutorial for using Pipiline module in scikit-learn library.

Deployment

• localHostDeploy: The ML Model local host deployment using Flask.
• dockerDeploy: The ML Model local host deployment using docker.

Computer vision

• KimJoug_unModel.ipynb: The face recognition model of Kim Jong-un with dlib library.
• LisaFaces.ipynb: The face recognition model with a few lines of code using face_recognition library.
• agePrediction.ipynb: The age prediction from image using age_net.caffemodel pre-trained model.
• face_recognition_pca_svm.ipynb: Building face recognition by using Principal Component Analysis (PCA) and Support Vector Machine (SVM).
• HOGClassification.ipynb: Building car logo classification model by using histogram of oriented gradients (HOG) with K-Nearest neighbor.
• slidingWindow.ipynb: Sliding window for image processing.
• nonMaximumSuppression.ipynb: Non-maximum suppression for true positive image processing.
• classicObjectDetection.ipynb: Apply HOG Features extraction with image sliding window and Non-maximum suppression to create object detection model.
• faceTracking.ipynb: Object tracking using FaceNet model for face detection. Then, using OpenCV as the tracker.
• faceMaskTiny: The face maks detection using YOLOV4-Tiny pre-trained model from Darknet.

Market risk

• sharpeRatio.ipynb: Portfolio optimisation using Sharpe ratio.

Customer and Marketing

• RFMAnalysis.ipynb: The customer segmentation with RFM Analysis.
• marketBasket.ipynb: The market basket analysis to uncover associations between items in the shop.
• basicCLV.ipynb: The basis Customer Lifetime Value (CLV).
• customerCohort.ipynb The customer behaviour model using Cohort analysis.

Natural language processing (NLP)

• reExample.py: The regular expression (RegEx) by python. To deal with text mining for NLP.
• twitterIO.ipynb: The TwitterIO data analytics to find inside topic of fake accounts by Information Operation (IO).
• twitterIOLSA.ipynb: The topic modelling of TwitterIO Dataset using LSA Model.

PySpark

• PySparkUsedcarData.ipynb: The basic data processing using PySpark library.

Others

• COVIDLogScale.ipynb: The plot of log-scale for COVID-19 Stop pandemic.
• ExcelWorkingfile.ipynb: The integration of python and Excel using XlsxWriter.
• RVModelRandomForest.ipynb: The used car residual values model using Random Forest Regression with Double Declining Balance (DDB) function.
• sir_seir_model.ipynb: The simulation model for COVID-19 pandemic.
• googleScraping.ipynb: The web-scraping by BeautifulSoup.
• interview.py: The question during interview process.
• waterfallChart.ipynb: The waterfall analysis plots by using matplotlib.