pejmanai-data-analysis 0.1.2

Description:

pejmanaidataanalysis 0.1.2

Pejmanai Data Analysis Package

Overview

pejmanai_data_analysis is a Python package for comprehensive data analysis, including read data in csv format, data preprocessing, data visualization, and machine learning modeling for both regression and classification problems. It provides tools to streamline the process of understanding and modeling datasets with ease.

Features

Data Reading: Load datasets from CSV files with error handling.
Data Description: View basic statistics, data information, and missing values.
Data Preprocessing: Handle missing values and encode categorical variables.
Data Visualization: Generate scatter plots, histograms, KDE plots, and heatmaps.
Regression Models: Evaluate multiple regression models including Linear Regression, Ridge Regression, Decision Trees, Random Forests, and K-Nearest Neighbors.
Classification Models: Compare various classification models such as Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, and MLP Classifiers.

Installation

You can install the package using pip:
pip install pejmanai_data_analysis

Usage

Data Reading


from pejmanai_data_analysis.app import read_csv
df = read_csv('path/to/your/data.csv')
print(df)


Data Description


from pejmanai_data_analysis.app import data_description
data_description('path/to/your/data.csv')


Data Preprocessing


from pejmanai_data_analysis.app import data_preprocessing
df_preprocessed = data_preprocessing('path/to/your/data.csv')
print(df_preprocessed)


Data Visualization


from pejmanai_data_analysis.app import data_visualization
data_visualization('path/to/your/data.csv', 'x_column', 'y_column')


Data Prediction (Regression)


from pejmanai_data_analysis.app import data_prediction
data_prediction('path/to/your/data.csv', 'target_column')


Data Classification


from pejmanai_data_analysis.app import data_classification
data_classification('path/to/your/data.csv', 'target_column')

License

This project is licensed under the MIT License

Contact
For any questions or feedback, please reach out to Pejman Ebrahimi.