Visual Design.

Top 6 machine learning algorithms (decision tree, random forest, naive bayes, KNN, SVM, logisitc regression) and how to build a machine learning model pipeline to address classification problems in python.

Log transformation, clipping methods, minmax scaler, standard scaler and robust scaler, visit Data Transformation and Feature Engineering in Python for full code walk-through.

Deep learning is a sub-category of machine learning models that uses neural networks. To build a Deep Learning Model, it follows the procedure of prepare, define, compile, fit and evaluate the model. 

Main EDA techniques: univariate analysis, multivariate analysis, and feature engineering ... visit "Semi-Automated Exploratory Data Analysis Process in Python" for full code walk-through.

This notebook provides a practical guide to implement linear regression, walking through the model building lifecycle: EDA, feature engineering, model implementation and model evaluation. Please visit article "A Practical Guide to Linear Regression" for step by step guide.

This is a step by step guide of implementing Logistic Regression model using Python library scikit-learn, including fundamental steps: Data Preprocessing, Feature Engineering, EDA, Model Building and Model Evaluation.

This code snippet includes the procedure of building a recommender system using KNN and SVD: 1. EDA for Recommender System 2. Collaborative Based Filtering Algorithms: K Nearest Neigbour vs. Singular Value Decomposition; 3. Model Evaluation: cross validation vs. train-test split; 4. Provide Top Recommendations

This code snippet includes the comparison of  four common types of regression models: Linear Regression, Lasso Regression, Ridge Regression, Polynomial Regression

Code snippet for an interactive guide to Statistical Power. A visual illustration of the relationship among Power, Type 1 error and Type 2 error. 

An interactive exploration that compares and visualizes the difference between three common statistical tests: T-test, ANOVA test and Chi-Squared test.

This code snippet covers ARMA, ARIMA and SARIMA models for time series analysis. Additionaly, explore concepts and techniques related to time series data, including Stationarity, ADF test, ACF/PACF plot and AIC.

This code snippet covers following three steps to get your hands on building your first web app and extend your data analytics projects to a self-served platform:

• Extract Data and Build Database

• Define Data Analytics Process as Functions

• Construct Web App Interface