Metadata-Version: 2.1
Name: future_sales_prediction_2024
Version: 0.2.2
Summary: A package for feature extraction, hyperopt, and validation schemas
Author: Polina Yatsko
Author-email: yatsko_polina1@mail.ru
License: MIT
Keywords: machine-learning xgboost hyperopt data-science regression
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7,<3.13
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: lightgbm
Requires-Dist: xgboost
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: hyperopt
Requires-Dist: shap
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: dvc
Requires-Dist: dvc-gs
Requires-Dist: google-auth
Requires-Dist: google-auth-oauthlib
Requires-Dist: google-cloud-storage

# Data Science: Sales Prediction

#### Project Status: Completed

## Overview:
This project provides a Python package,future_sales_prediction_2024, to simplify common tasks in data science workflows. It includes tools for feature extraction, validation schema creation, hyperparameter optimization, and model training.


### Methods Used
* Feature Engineering: Automates the creation and selection of important features, including memory optimization.
* Validation: Implements schema validation to ensure data consistency, identify missing values, and prevent duplicate records.
* Hyperparameter Tuning: Leverages tools like hyperopt for efficient parameter search.
* Visualization: Includes plotting tools for feature importance and error analysis.

### Technologies
* Python
* Pandas, jupyter

### Data Sources:
The tools in this package are designed to work with structured datasets, such as CSV files. For example, it can handle datasets used in machine learning competitions like Kaggle or any tabular data source.

### Challenges:
* Complexity in Generalization: Making the tools generic enough to work with diverse datasets while maintaining simplicity.
* Performance Optimization: Balancing ease of use with computational efficiency.
* Error Handling: Ensuring clear and helpful error messages for data validation and model failures.

### Conclusion:
This package is a modular and flexible solution for streamlining data science workflows. It provides data scientists and ML engineers with reusable tools to focus on solving domain-specific problems.

## [0.1.1] - 2024-11-25
### Added
- Changes in loader function: upload files using filenames.

## [0.2.1] - 2024-11-26
- Added support for Google Cloud Storage.
- Improved deployment pipeline.
- Bug fixes and performance improvements.

## [0.2.1] - 2024-11-27
- Bug fixes.
