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Python

Time Series Analysis

Python

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.

Python, TensorFlow

Deep Learning

Python, TensorFlow

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. 

Python

Statistical Power

Python

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

Python

Statistical Tests

Python

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

Python

Regression Models

Python

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

Python

Classification Models

Python

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.

Python

Recommendation System

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

Python

Linear Regression

Python

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.

Python

Logistic Regression

Python

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.


Python

Exploratory Data Analysis (EDA)

Python

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

Python

Data Transformation

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.

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