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Dabl

Dabl is a data analysis baseline library that makes machine learning modeling easier. It includes features that can be processed, analyzed, and modeled in just a few lines of Python code.

Installation

Pip install dabl1, data preprocessing

Dabl automatically executes the data preprocessing pipeline in several lines of Python code. The preprocessing steps performed by dabl include identifying missing values, deleting redundant features, and understanding the data types of features to further perform feature engineering.

The list of feature types detected by dabl includes:

Continuous

Categorical

Date

Dirty_float

Low_card_int

Free_string

Useless

Dabl uses one line of Python code to automatically classify all dataset features into the above data types.

Df_clean = dabl.clean (df, verbose=1)

The original Titanic dataset has 12 features, and dabl automatically classifies them into the above data types for further feature engineering. Dabl also provides the ability to change the data type of any feature according to your requirements.

Db_clean = dabl.clean (db, type_hints= {"Cabin": "categorical"})

You can use the detect_types () function to view the data types assigned to each feature.

2. Exploratory data analysis

EDA is an important part of the life cycle of data science model development. Seaborn, Matplotlib, and so on are visualization libraries that perform various analyses to better understand data sets. Dabl makes EDA very simple and saves a lot of time.

Dabl.plot (df_clean, target_col= "Survived")

The plot () function in dabl can be visualized by drawing various graphs, including:

Bar chart of target distribution

Scatter point pair graph

Linear discriminant analysis

Dabl automatically performs PCA on the dataset and displays discriminant PCA diagrams of all features in the dataset.

3. Modeling

Dabl trains various baseline machine learning algorithms on the training data to speed up the modeling workflow and return the models with the best performance. Dabl makes simple assumptions and generates metrics for the baseline model.

You can model using the SimpleClassifier () function in dabl, which quickly returns the best model.

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