<h1>Data Analysis CheatSheet (everything you might need )</h1>
<p>
The project include four modules with functions to summarize and analysis dataset in order
to understand properties and relationships.
[Read Documentation](https://tamer-george.github.io/docs/_build/html/index.html)
</p>
<h2>1. DESCRIPTIVE MODULE</h2>
<h3>Answer the question (What Happen?)</h3>
Includes two modules:
- <i>pyeda.py</i><br>
Includes simple EDA functions that might be useful in general cases.<br><br>
- <i>pydatapreprocessing.py</i><br>
Useful Functions for data preprocessing.<br>
For Example:
from descriptive import pyeda
pyeda.visualize_different_plots_of_numeric_col(data_frame, column_name: str)
For Example:
from descriptive import pydatapreprocessing
pydatapreprocessing.standardization_numerical_columns(data, numerical_column: list)
<h2>2. DIAGNOSTIC MODULE</h2>
<h3>Answer the question (Why did it happen?)</h3>
<i>pydiagnostic.py</i><br><br>
Include functions to apply hypothesis testing
For Example:
from diagnostic import pydiagnostic
pydiagnostic.display_test_p_value(p_value: float)
<h2>3. PREDICTIVE MODULE </h2>
<h3>Answer the question (What is likely to Happen?)</h3>
<i>pypredictive.py</i> <br><br>
The module include functions for:
* Regression
* Forcasting
* Classification
* Clustering
For Example:
from predictive import pypredictive
pypredictive.chi_value_plot(data)
<h2>4. PRESCRIPTIVE MODULE</h2>
<h3>Answer the question (What action should we take?)</h3>
<i>pyprescriptive.py</i><br>
Module include functions that might be useful for classification
For Example:
from prescriptive import pyprescriptive
pyprescriptive.visualize_confusion_matrix(actual, predicted)
Raw data
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