Bhishan Poudel

NOTE:
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Chapter: Personal Module For Data Analysis: bp

Personal Module: bp

A01: What does the Module Do? (GIF videos)

API usage

Data Visualization

Data Visualization using Plotly

Statistics

Model Evaluation

Time Series Analysis

Miscellaneous Usage

A02: Detailed Use of Module bp (Jupyter Notebooks)

Data Description

Data Visualization

Data Visualization using Plotly

EDA using Plotly

Miscellaneous Plots

Matplotib Styles

Deal with Colors

Statistics

Timeseries Analysis

Model Evaluation

Chapter 1: Data Science Projects

Section A: Regression

A01: King County Seattle House Price Prediction (Regression)

GitHub

README

Statistics Report

a01 Data Processing

a02 Data processing Script

a03 Regression Statistics

a04 Regression EDA

a05 Regression EDA: bokeh

a06 Regression EDA: plotly

a07 Regression EDA: pixiedust

a08 Regression EDA: pandas profiling

b01 Regression Modelling (Boosting): Hist Gradient Boosting

b02 Regression Modelling (Boosting): XGBoost

b03 Regression Modelling (Boosting): LightGBM

b04 Regression Modelling (Boosting): CatBoost

e01 Regression Modelling (Ensemble): Stacking and Blending

m01 Regression Modelling (sklearn): linear and polynomial regression

m02 Regression Modelling (sklearn): sklearn methods

m03 Regression Modelling (sklearn): Random Forest

m04 Regression Modelling (statsmodels): linear OLS

s01 Regression Modelling (Special): pycaret

s02 Feature Engineering (Featuretools): XGBoost

s03 Feature Engineering (Featuretools): LightGBM

s04 Feature Engineering (Featuretools): CatBoost

w01 Model Interpretation: Yellowbrick, Lime, Eli5

w02 Model Interpretation: What If Tool (WIT)

w03 Model Interpretation: Dalex

w04 Model Interpretation: Dtreeviz

x01 Big Data Analysis: PySpark

x02 Big Data Analysis: PySpark Random Forest Tuning

y01 DeepLearning: Keras

z01 Best Model: CatBoost

z02 Best Model: XGBoost

z03 Best Model: Overall

A02: All State Insurance (Insurance: Regression)

GitHub

README

a01 Exploratory Data Analysis

a02 Data Processing

b01 Modelling

b02 Modelling Pyspark

Section B: Classification

BX.01: Fraud Detection (Binary Classification)

GitHub

README

Deploy End to End Machine Learning Model (Fraud Detection) on Heroku

a01 Classification EDA

a02 Classification Statistics

b01a Classification Modelling (Boosting): XGBboost

b01b Classification Modelling (Boosting): XGBboost (HPO)

b01c Classification Modelling (Boosting): XGBboost Custom Loss

b02 Classification Modelling (Boosting): LightGBM

b03 Classification Modelling (Boosting): Catboost

b03b Classification Modelling (Boosting): Catboost Custom Loss

e01 Classification Modelling (Ensemble): Stacking

m01 Classification Modelling (sklearn): Undersampling

m02 Classification Modelling (sklearn): Logistic Regression SMOTE

m03 Classification Modelling (sklearn): Decision Tree

m04 Classification Modelling (sklearn): Calibrated Classification

b05 Classification Modelling (sklearn): Isolation Forest and LOF

s01 Classification Modelling (Special): pycaret (lda)

s02 Classification Modelling (Special): evalML

x01 Classification Modelling (Big Data): dask

x02 Classification Modelling (Big Data): vaex

x03 Classification Modelling (Big Data): pySpark

y01 Classification Modelling (Deep Learning): keras simple model

y02 Classification Modelling (Deep Learning): keras large model

y03 Classification Modelling (Deep Learning): keras oversampling

y04 Classification Modelling (Deep Learning): keras classifier sklearn api

y05 Classification Modelling (Deep Learning): keras classifier (Keras tuner)

BX.02: Customer Churn (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

a01 Exploratory Data Analysis (Plolty)

a02 Customer Churn: Data Processing

bx01 Modelling (Boosting): XGBoost with HyperbandCV

bx02 Modelling (Boosting): XGBoost with Bayes Optimization

bl01 Modelling (Boosting): LightGBM Classifier with sklearn pipeline and HyperbandCV

bl02 Modelling (Boosting): LightGBM Classifier with Optuna HPO

bl03 Modelling (Boosting): LightGBM Classifier with Hyperopt HPO

bc01 Modelling (Boosting): CatBoostClassifier with optuna hyperparameter tuning

ml01 Modelling (Sklearn): LogisticRegression

ml02 Modelling (Sklearn): LogisticRegressionCV

splr01 Modelling (Special): (Pycaret) Logistic Regression

spn01 Modelling (Special): (Pycaret) Naive Bayes

spx01 Modelling (Special): (Pycaret) Xgboost

spdla01 Modelling (Special): (Pycaret) Linear Discriminant Analysis

sflr01 Modelling (Special): (featuretools) Logistic Regression

se01 Modelling (Special): (evalml) Built-in Algorithm

w01 Model Interpretation: (What If Tool) Logistic Regression

wbl Model Interpretation: (LOFO) Logistic Regression

w01 Model Interpretation: (Interpret) Builtin Estimators Logistic Regression and Boosting

y01 Deep Learning: (Keras) Sequential Simple Model

BX.03: Porto Seguro Auto Insurance (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

a02 Modelling: LightGBM

a03 Modelling: XGBoost

a04 Modelling: Keras Entity Embedding

a05 Modelling: Stacking different Models

a06 Feature Selection: Boruta and Target Permutation

BX.04: Breast Cancer Wisconsin (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

b01 Modlling: (Boosting) XGBoost

y01 Deep Learning: Keras Sequential with class_weight

y02 Deep Learning: Keras Sequential

BY.01: Prudential Insurance (Multiclass Classification)

GitHub

README

a01 Exploratory Data Analysis

a02 Multiclass Classification Statistics

a03 Data Preprocessing

a04 Data Preprocessing Script

b01 Modelling: Linear Regression

b02 Modelling: RF Classifier

b03 Modelling: RF Classifier AUC ROC

b04 Modelling: XGBoost Multiclass Classification

b05 Modelling: XGBoost Linear Regression and Poisson Regression with Offset

c01 Multiclass Model Interpretation: eli5, shap and pdpbox

Section C: Clustering

C01: Clinical Features and Biomarkers Analysis for Diabetes (Clustering)

a01 Data Preparation

a02 Statistical Study of Features

b01 Analysis of Clinical Features

b02 Analysis of Biomarkers

m01 Modelling: Diabetes Classification

m02 Modelling: Clustering

Clustering of Covid Samples MSA

Section D: Timeseries Analysis

D01: Timeseries Analysis for Web Traffic Data

GitHub

README

a01 Data Processing

b01 Timeseries visualization and eda

c01 Timeseries statistics

d01 Timeseries modelling: ARIMA

d02 Timeseries modelling: VAR

e01 Timeseries modelling: sklearn

f01 Timeseries modelling: tsfresh and xgboost

g01 Timeseries modelling: fbprophet

g02 Timeseries modelling: fbprophet holidays

h01 Timeseries modelling: deep learning

Section E: Natural Language Processing (NLP)

E01: Twitter Sentiment Analysis (Analytics Vidhya Hackathon: Identify the Sentiment)

GitHub

a00 README

a01 Text Data Processing

a02 Text Data EDA

a03 Scattertext for positive and negative sentiments

a03b Result: Twitter Sentiment Html

b01 Text Data Modelling: BoW + Word2Vec + TF-IDF

b02 Text Data Modelling: TF-IDF + Logistic Regression

c01 Sentiment Analysis: ktrain

c01 Sentiment Analysis: ktrain, neptune

c01 Sentiment Analysis: ktrain, neptune HPO

c02 Sentiment Analysis: simpletransformers + Roberta

d01 Sentiment Analysis: (keras) LSTM

d02 Sentiment Analysis: (keras) GRU, CNN, LSTM

e01 Sentiment Analysis: (transformers) Small data with torch and distilbert

e02 Sentiment Analysis: (transformers): Full data with keras and distilbert

e03 Sentiment Analysis: BERT and Tensorflow

e03 Sentiment Analysis: BERT, Tensorflow, and Neptune

E02: Toxic Comments (Multiclass Text Classification)

GitHub

README

a01 Text Data Processing

a02 Text Data EDA

a03 Text Data EDA: Plotly

m01 Text Data Binary Classification (Toxic or not)

s01 Named Entity Recognition and Dependency Parsing: spacy2

s01 Named Entity Recognition and Dependency Parsing: spacy3

y01 Deep Learning: GRU and Fasttext

y01b Deep Learning: GRU, Fasttext, Badwords

y02 Deep Learning: Transformers PyTorch BERT

y02b Deep Learning: Transformers PyTorch XLNET

y02c Deep Learning: Transformers PyTorch DisltilBert

y03 Bert Client: XGBoost

y03b Bert Client: Keras Sequential

E03: Consumer Complaints (Multiclass Text Classification)

GitHub

README

a01 Text Processing

a02 EDA for Text Data

b01 Text Data Modelling: Tf-idf and Sklearn Classifiers

b02 Text Data Modelling: LinearSVC

c01 Model Evaluation: Yellowbrick

c02 Model Evaluation: scikit-plot

d01 Text Data Modelling: PySpark

e01 Text Data Modelling: simpletransformers

Section F: Insurance Data Modelling

F01: French Motor Claims (Pure Premium Modelling)

GitHub

README

a01 Data Cleaning

b01 Frequency Modelling (Poisson Regressor)

b02 Severity Modelling (Gamma Regressor)

b03 Pure Premium Modelling (Tweedie Regressor)

b04 Tweedie Model vs FrequencySeverity Model

b05 Lorentz Curves Comparison

c01 Xgboost with Tweedie Regression

d01 GAM Linearized Modelling using Pygam

Section G: Financial Data Analysis

G01: Credit Risk (Banking: Financial Modelling (Scorecard))

GitHub

README

a01 EDA for Credit Risk Data

a02 Data Processing

b01 Risk Modelling: PDModel Gini KS CreditScore Scorecard

Chapter 2: SQL

2A: SQLITE Queries for Northwind Database (Book: SQL Practice Problems by Vasilik)

a01 Beginner Level Problems (1-19)

a02 Intermediate Level Problems (20-31)

a03 Advanced Level Problems (32-57)

2B.01: SQL Queries for Hospital Management Database

a01 SQL Queries using postgres

a02 SQL Queries using postgres, sqlalachemy and pandas

a03 SQL Queries using sqlite3

2B.02: SQL Queries for Computer Store Database

a01 SQL Queries using postgres

a02 SQL Queries using sqlite

2B.03: SQL Queries for Employee Management Database

a01 SQL Queries using postgres and pyspark

2B.04: SQL Queries for the Warehouse Database

a01 SQL Queries using postgres

a02 SQL Queries using pyspark and postgres

2B.05: SQL Queries for Movie Theaters Database

a01 SQL Queries using pyspark and postgres

a02 SQL Queries using pyspark and sqlite

2B.06: SQL Queries for Pieces and Providers Database

a01 SQL Queries using postgresql

a02 SQL Queries using pyspark, sqlite and sqlalchemy

Chapter 3: Business Projects

3.01: Spanish Translation A/B Testing

GitHub

README

a01 Spanish Translation A/B Testing with Extensive EDA and Statistical Tests

3.02: Customer Lifetime Value

GitHub

README

a01 Data Cleaning

b01 Modelling: BG/NBD and Gamma-Gamma Distribution

b02 Modelling: Keras Modelling and XGBoost

=========== NOTE: The Section Below is the for the Expanded Version of All the Links Above =========

NOTE:
If we are using Safari, we can expand the collapsible links in above section. But for other browsers, such as Google Chrome, the links may not be expanded. In that case, I have included all the expanded version of the links in the below section.

My Personal Module: bp

Data Description

Data Visualization

Data Visualization using Plotly

EDA using Plotly

Miscellaneous Plots

Matplotib Styles

Deal with Colors

Statistics

Timeseries Analysis

Model Evaluation

A01: King County Seattle House Price Prediction (Regression)

GitHub

README

Statistics Report

a01 Data Processing

a02 Data processing Script

a03 Regression Statistics

a04 Regression EDA

a05 Regression EDA: bokeh

a06 Regression EDA: plotly

a07 Regression EDA: pixiedust

a08 Regression EDA: pandas profiling

b01 Regression Modelling (Boosting): Hist Gradient Boosting

b02 Regression Modelling (Boosting): XGBoost

b03 Regression Modelling (Boosting): LightGBM

b04 Regression Modelling (Boosting): CatBoost

e01 Regression Modelling (Ensemble): Stacking and Blending

m01 Regression Modelling (sklearn): linear and polynomial regression

m02 Regression Modelling (sklearn): sklearn methods

m03 Regression Modelling (sklearn): Random Forest

m04 Regression Modelling (statsmodels): linear OLS

s01 Regression Modelling (Special): pycaret

s02 Feature Engineering (Featuretools): XGBoost

s03 Feature Engineering (Featuretools): LightGBM

s04 Feature Engineering (Featuretools): CatBoost

w01 Model Interpretation: Yellowbrick, Lime, Eli5

w02 Model Interpretation: What If Tool (WIT)

w03 Model Interpretation: Dalex

w04 Model Interpretation: Dtreeviz

x01 Big Data Analysis: PySpark

x02 Big Data Analysis: PySpark Random Forest Tuning

y01 Deep Learning: Keras

z01 Best Model: CatBoost

z02 Best Model: XGBoost

z03 Best Model: Overall

A02: All State Insurance (Insurance: Regression)

GitHub

README

a01 Exploratory Data Analysis

a02 Data Processing

b01 Modelling

b02 Modelling Pyspark

Section B: Classification

BX.01: Fraud Detection (Binary Classification)

GitHub

README

Deploy End to End Machine Learning Model (Fraud Detection) on Heroku

a01 Classification EDA

a02 Classification Statistics

b01a Classification Modelling (Boosting): XGBboost

b01b Classification Modelling (Boosting): XGBboost (HPO)

b01c Classification Modelling (Boosting): XGBboost

b02 Classification Modelling (Boosting): LightGBM

b03 Classification Modelling (Boosting): Catboost

b03b Classification Modelling (Boosting): Catboost Custom Loss

e01 Classification Modelling (Ensemble): Stacking

m01 Classification Modelling (sklearn): Undersampling

m02 Classification Modelling (sklearn): Logistic Regression SMOTE

m03 Classification Modelling (sklearn): Decision Tree

m04 Classification Modelling (sklearn): Calibrated Classification

b05 Classification Modelling (sklearn): Isolation Forest and LOF

s01 Classification Modelling (Special): pycaret (lda)

s02 Classification Modelling (Special): evalML

x01 Classification Modelling (Big Data): dask

x02 Classification Modelling (Big Data): vaex

x03 Classification Modelling (Big Data): pySpark

y01 Classification Modelling (Deep Learning): keras simple model

y02 Classification Modelling (Deep Learning): keras large model

y03 Classification Modelling (Deep Learning): keras oversampling

y04 Classification Modelling (Deep Learning): keras classifier sklearn api

y05 Classification Modelling (Deep Learning): keras classifier (Hyperparameter tuning)

BX.02: Customer Churn (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

a01 Exploratory Data Analysis (Plolty)

a02 Customer Churn: Data Processing

bx01 Modelling (Boosting): XGBoost with HyperbandCV

bx02 Modelling (Boosting): XGBoost with Bayes Optimization

bl01 Modelling (Boosting): LightGBM Classifier with sklearn pipeline and HyperbandCV

bl02 Modelling (Boosting): LightGBM Classifier with Optuna HPO

bl03 Modelling (Boosting): LightGBM Classifier with Hyperopt HPO

bc01 Modelling (Boosting): CatBoostClassifier with optuna hyperparameter tuning

ml01 Modelling (Sklearn): LogisticRegression

ml02 Modelling (Sklearn): LogisticRegressionCV

splr01 Modelling (Special): (Pycaret) Logistic Regression

spn01 Modelling (Special): (Pycaret) Naive Bayes

spx01 Modelling (Special): (Pycaret) Xgboost

spdla01 Modelling (Special): (Pycaret) Linear Discriminant Analysis

sflr01 Modelling (Special): (featuretools) Logistic Regression

se01 Modelling (Special): (evalml) Built-in Algorithm

w01 Model Interpretation: (What If Tool) Logistic Regression

wbl Model Interpretation: (LOFO) Logistic Regression

w01 Model Interpretation: (Interpret) Builtin Estimators Logistic Regression and Boosting

y01 Deep Learning: (Keras) Sequential Simple Model

BX.03: Porto Seguro Auto Insurance (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

a02 Modelling: LightGBM

a03 Modelling: XGBoost

a04 Modelling: Keras Entity Embedding

a05 Modelling: Stacking different Models

a06 Feature Selection: Boruta and Target Permutation

BX.04: Breast Cancer Wisconsin (Binary Classification)

GitHub

README

a01 Exploratory Data Analysis

b01 Modlling: (Boosting) XGBoost

y01 Deep Learning: Keras Sequential with class_weight

y02 Deep Learning: Keras Sequential

BY.01: Prudential Insurance (Multiclass Classification)

GitHub

README

a01 Exploratory Data Analysis

a02 Multiclass Classification Statistics

a03 Data Preprocessing

a04 Data Preprocessing Script

b01 Modelling: Linear Regression

b02 Modelling: RF Classifier

b03 Modelling: RF Classifier AUC ROC

b04 Modelling: XGBoost Multiclass Classification

b05 Modelling: XGBoost Linear Regression and Poisson Regression with Offset

c01 Multiclass Model Interpretation: eli5, shap and pdpbox

Section C: Clustering

C01: Clinical Features and Biomarkers Analysis for Diabetes (Clustering)

a01 Data Preparation

a02 Statistical Study of Features

b01 Analysis of Clinical Features

b02 Analysis of Biomarkers

m01 Modelling: Diabetes Classification

m02 Modelling: Clustering

s01 Big Data: Modelling Diabetes Data Using Vaex

C02: Clustering of Grocery Items (Clustering)

Clustering of Grocery Items

C03: Clustering of Agriculture Data (Clustering)

Clustering of Agriculture Data

C04: Clustering Covid Samples Multiple Sequence Alignment (Clustering)

Clustering Covid Samples Multiple Sequence Alignment

Section D: Timeseries Analysis

D01: Timeseries Analysis for Web Traffic Data

GitHub

README

a01 Data Processing

b01 Timeseries visualization and eda

c01 Timeseries statistics

d01 Timeseries modelling: ARIMA

d02 Timeseries modelling: VAR

e01 Timeseries modelling: sklearn

f01 Timeseries modelling: tsfresh and xgboost

g01 Timeseries modelling: fbprophet

g02 Timeseries modelling: fbprophet holidays

h01 Timeseries modelling: deep learning

Section E: Natural Language Processing (NLP)

E01: Twitter Sentiment Analysis (Analytics Vidhya Hackathon: Identify the Sentiment)

GitHub

a00 README

a01 Text Data Processing

a02 Text Data EDA

a03 Scattertext for positive and negative sentiments

a03b Result: Twitter Sentiment Html

b01 Text Data Modelling: BoW + Word2Vec + TF-IDF

b02 Text Data Modelling: TF-IDF + Logistic Regression

c01 Sentiment Analysis: ktrain

c01 Sentiment Analysis: ktrain, neptune

c01 Sentiment Analysis: ktrain, neptune HPO

c02 Sentiment Analysis: simpletransformers + Roberta

d01 Sentiment Analysis: (keras) LSTM

d02 Sentiment Analysis: (keras) GRU, CNN, LSTM

e01 Sentiment Analysis: (transformers) Small data with torch and distilbert

e02 Sentiment Analysis: (transformers): Full data with keras and distilbert

e03 Sentiment Analysis: BERT and Tensorflow

e03 Sentiment Analysis: BERT, Tensorflow, and Neptune

E02: Toxic Comments (Multiclass Text Classification)

GitHub

README

a01 Text Data Processing

a02 Text Data EDA

a03 Text Data EDA: Plotly

m01 Text Data Binary Classification (Toxic or not)

s01 Named Entity Recognition and Dependency Parsing: spacy2

s01 Named Entity Recognition and Dependency Parsing: spacy3

y01 Deep Learning: GRU and Fasttext

y01b Deep Learning: GRU, Fasttext, Badwords

y02 Deep Learning: Transformers PyTorch BERT

y02b Deep Learning: Transformers PyTorch XLNET

y02c Deep Learning: Transformers PyTorch DisltilBert

y03 Bert Client: XGBoost

y03b Bert Client: Keras Sequential

E03: Consumer Complaints (Multiclass Text Classification)

GitHub

README

a01 Text Processing

a02 EDA for Text Data

b01 Text Data Modelling: Tf-idf and Sklearn Classifiers

b02 Text Data Modelling: LinearSVC

c01 Model Evaluation: Yellowbrick

c02 Model Evaluation: scikit-plot

d01 Text Data Modelling: PySpark

e01 Text Data Modelling: simpletransformers

Section F: Insurance Data Modelling

F01: French Motor Claims (Pure Premium Modelling)

GitHub

README

a01 Data Cleaning

b01 Frequency Modelling (Poisson Regressor)

b02 Severity Modelling (Gamma Regressor)

b03 Pure Premium Modelling (Tweedie Regressor)

b04 Tweedie Model vs FrequencySeverity Model

b05 Lorentz Curves Comparison

c01 Xgboost with Tweedie Regression

d01 GAM Linearized Modelling using Pygam

Section G: Financial Data Analysis

G01: Credit Risk (Banking: Financial Modelling (Scorecard))

GitHub

README

a01 EDA for Credit Risk Data

a02 Data Processing

b01 Risk Modelling: PDModel Gini KS CreditScore Scorecard

Chapter 2: SQL

2A: SQLITE Queries for Northwind Database (Book: SQL Practice Problems by Vasilik)

a01 Beginner Level Problems (1-19)

a02 Intermediate Level Problems (20-31)

a03 Advanced Level Problems (32-57)

2B.01: SQL Queries for Hospital Management Database

a01 SQL Queries using postgres

a02 SQL Queries using postgres, sqlalachemy and pandas

a03 SQL Queries using sqlite3

2B.02: SQL Queries for Computer Store Database

a01 SQL Queries using postgres

a02 SQL Queries using sqlite

2B.03: SQL Queries for Employee Management Database

a01 SQL Queries using postgres and pyspark

2B.04: SQL Queries for the Warehouse Database

a01 SQL Queries using postgres

a02 SQL Queries using pyspark and postgres

2B.05: SQL Queries for Movie Theaters Database

a01 SQL Queries using pyspark and postgres

a02 SQL Queries using pyspark and sqlite

2B.06: SQL Queries for Pieces and Providers Database

a01 SQL Queries using postgresql

a02 SQL Queries using pyspark, sqlite and sqlalchemy

Chapter 3: Business Projects

3.01: Spanish Translation A/B Testing

GitHub

README

a01 Spanish Translation A/B Testing with Extensive EDA and Statistical Tests

3.02: Customer Lifetime Value

GitHub

README

a01 Data Cleaning

b01 Modelling: BG/NBD and Gamma-Gamma Distribution

b02 Modelling: Keras Sequential and XGBoost

Chapter 4: Personal Module "bp"

The module "bp" expands pandas DataFrame API and adds various visualization and data analysis functionalities. For example, we can get a plot of a numeric column using the method "df.bp.plot_num("my_numeric_variable")".

Currently my module contains following methods:

1	2	3	4
BPAccessor	hlp	plot_daily_cat	plotly_corr_heatmap
Plotly_Charts	json	plot_date_cat	plotly_countplot
RandomColor	light_axis	plot_ecdf	plotly_country_plot
add_interactions	lm_plot	plot_gini	plotly_distplot
add_text_barplot	lm_residual_corr_plot	plot_ks	plotly_histogram
adjustedR2	lm_stats	plot_multiple_jointplots_with_pearsonr	plotly_mapbox
hex_to_rgb	magnify	plot_num	plotly_radar_plot
discrete_cmap	multiple_linear_regression	plot_num_cat	plotly_scattergl_plot
display_calendar	no_axis	plot_num_cat2	plotly_scattergl_plot_colorcol
plot_plot_binn	optimize_memory	plot_num_num	plotly_scattergl_plot_subplots
find_corr	parallelize_dataframe	plot_pareto	plotly_usa_bubble_map
freq_count	parse_json_col	plot_roc_auc	plotly_usa_map
get_binary_classification_report	partial_corr	plot_roc_skf	plotly_usa_map2
get_binary_classification_scalar_metrics	plot_boxplot_cats_num	plot_simple_linear_regression	point_biserial_correlation
get_binary_classification_scalar_metrics2	plot_cat	plot_statistics	print_calendar
get_column_descriptions	plot_cat_binn	plot_stem	print_confusion_matrix
get_distinct_colors	plot_cat_cat	plot_two_clusters	print_df_eval
get_false_negative_frauds	plot_cat_cat2	plotly_agg_country_plot	print_statsmodels_summary
get_high_correlated_features_df	plot_cat_cat_pct	plotly_agg_usa_plot	random
get_mpl_style	plot_cat_num	plotly_binary_clf_evaluation	regression_residual_plots
get_outliers	plot_confusion_matrix_plotly	plotly_boxplot	remove_outliers
get_outliers_kde	plot_corr	plotly_boxplot_allpoints_with_outliers	rgb2hex
get_plotly_colorscale	plot_corr_style	plotly_boxplot_categorical_column	select_kbest_features
get_yprobs_sorted_proportions	plot_corrplot_with_pearsonr	plotly_bubbleplot	show_methods

Chapter 5: Overview of My Projects (GIF Videos)

- Click on the box to show the GIF video. (The box moves down and GIF video plays above it.)
- Go to the buttom of the gif and click the same button again to hide the video.