YouTube Analytics Web App
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
Time Series Analysis
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.
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.
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 includes the comparison of four common types of regression models: Linear Regression, Lasso Regression, Ridge Regression, Polynomial Regression
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.
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 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.
Exploratory Data Analysis (EDA)
Main EDA techniques: univariate analysis, multivariate analysis, and feature engineering ... visit "Semi-Automated Exploratory Data Analysis Process in Python" for full code walk-through.
Log transformation, clipping methods, minmax scaler, standard scaler and robust scaler, visit Data Transformation and Feature Engineering in Python for full code walk-through.
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