95 skills found · Page 4 of 4
mpasternak / Pdf Table ExtractorExtract tabular data from PDF files in Python
rngzhi / Pdf2tablepdf2table is a Python library designed to extract tabular data from PDF files and images efficiently and accurately.
eleprocha / Benford S Law Python Codea Python program that loads numerical data, records the frequency of occurrence of the first digits, compares these to Benford’s law using the chisquare goodnessoffit test, and presents the comparison in both tabular and graphical form.
CeciPani / MARLENAA python library to agnostically explain multi-label black-box classifiers (tabular data)
michaelsweeney / EpparsePython module for reading and visualization EnergyPlus SQL files for both tabular and time-series/hourly results.
NLPOptimize / FormatFlex🚀 A flexible Python library for easy handling and conversion of Hierarchical, Tabular, and Serialized data formats.
durden / Tablib(Work in Progress) A format-agnostic tabular dataset library for Python.
ubermag / UbermagtablePython package for manipulating tabular data.
enRichMyData / Lion LinkerPython library that uses LLMs to perform entity linking over tabular data
thombashi / Tabledatatabledata is a Python library to represent tabular data.
0xdolan / PyrojKurdish solar calendar and conversions (Gregorian, Persian/Jalali, tabular Islamic) using the Python standard library only.
MariamGado0 / Starbucks Capstone Project ML Udacity Aws# Starbucks Promotions Project ### This project is the Capstone Project of Udacity's Machine Learning Engineering Nanodegree program.    ## Problem Statement This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products. Starbucks collects the customer data to understand their behaviour on the rewards and offers sent via the mobile-app. Once every few days, Starbucks sends the personalised offers to its customers. These customers can respond positively/negatively/neutrally. A key thing to note is that not all the customers receive the same offer. The task of this project is to combine transaction, demographic and offer data of the past (which is already provided) to determine which demographic groups respond best to which offer types. In order to develop this project, we needed to use some tools, packages, systems and services that could help us achieve our goals. #### Libraries First of all, we used **Python** to write our scripts not only for algorithm training and serving but also for the orchestration of the whole process. Important packages within this environment are listed below: This project is developed in Python 3.6. You will need install some libraries in order to run the code. Libraries are: * `pandas` so we could work with tabular data in dataframes; * `Ploty` so we could visualize our Dataset; * `matplotlib` for Dataset visualization; * `numpy` so we could easily manipulate arrays and data structures; * `seaborn` and `matplotlib` so we could generate insightful visualizations; * `sklearn` so we could build and develop our model pipeline; * `imblearn` so we could apply SMOTE to our training data; * `xgboost` so we could have our main classifier; * `sagemaker` so we could easily interact with AWS. * `json` for reading our Dataset Files. * `boto3` Finally, we used AWS environment in order to launch training jobs, deploy our model and serve predictions. The main services used are also listed below: * __AWS SageMaker__: training, hyperparameter tuning and endpoint serving; * __Amazon S3__: saving our data and model artifacts; ## Files Descriptions This project is structured as follows: #### 01. Proposal Project proposal documentation. #### 02. Data_Cleaning_[Dataset] Folder to perform data preparation and Dataset Cleaning and Prepare the Final Data for Further using in model algorithms. #### 03. Pre-processing Dataset Visualization Folder to perform final Pre-processing Dataset to be used in Visualization and exploration. #### 04. Dataset_Visualization Folder to perform Visualizations for the Pre-processed Dataset. #### 06. ORG_Starbucks_Capstone_Project.ipynb Jupyter notebook file that deploy final model and create an endpoint and orchestrates the end-to-end process in AWS SageMaker and also interacts with other services.
yuvii-b / Pdf To Xlsx LINUXPython script to convert tabular data in pdf files to csv(xlsx) format
DataCanvasIO / Tabular ToolboxA library of extension and helper modules for tabular data base on python's machine learning frameworks.
risc-mi / CatabraCaTabRa is a Python package for analyzing tabular data in a largely automated way. This includes generating descriptive statistics, creating out-of-distribution detectors, training prediction models for classification and regression tasks, and evaluating/explaining/applying these models on unseen data.
need47 / Dataframe TextualA powerful, interactive terminal-based viewer/editor for CSV/TSV/Excel/Parquet/Vortex/JSON/NDJSON built with Python, Polars, and Textual. Inspired by VisiData, this tool provides smooth keyboard navigation, data manipulation, and a clean interface for exploring tabular data directly in terminal with multi-tab support for multiple files!
thombashi / Tblfakertblfaker is a Python library to generate fake tabular data.
aryadhruv / LLMWorkbookLLMWorkbook is a Python package that integrates Large Language Models (LLMs) with tabular datatypes - workbooks and dataframes for seamless data analysis and automation.
Aniket21mathur / Web Scrapperpython code scrapping data from website and presenting it in tabular form
KoenvdBerg / Csv ValidatorValidates tabular CSV data using predefined validations, inspired from its Python homologue "Great Expectations".
