datapreparation

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Make your dataset talk to you. The AI assistant for data preparation.

A Holistic Platform for Automating Data Preparation

This repo includes codes for ML Zoomcamp. If you can follow the tutorials from the link here: https://www.youtube.com/watch?v=rowoDjPc8HU&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR 👩🏼‍💻

Public repository for custom blocks for Omniscope

Trifacta Flows Examples and Templates. Flows zip files, recipes and datasets.

ScrapySub is a Python library designed to recursively scrape website content, including subpages. It fetches the visible text from web pages and stores it in a structured format for easy access and analysis. This library is particularly useful for NLP and AI developers who need to gather large amounts of web content for their projects.

Learn data visualization through Tableau 2020 and create opportunities for you or key decision-makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. This Course on Udemy

mltrons dptron: Dirty Data in, Clean Data Out!

The Bikes Sales Analysis Excel Project is a practical exploration of sales data analysis using Microsoft Excel. This project showcases how Excel can be a powerful tool for data cleaning, preprocessing, visualization, and dashboard creation, all within a familiar spreadsheet environment.

Data Science materials

This repository demonstrates data imputation using Scikit-Learn's SimpleImputer, KNNImputer, and IterativeImputer.

The project deals with determining and predicting the type of accident taking place in the city of Austin. The data would help in understanding what possible factors are leading to the accidents based on the severity of the incident that has occurred.

For a real estate firm, building a house price prediction model based upon various factors. Problem - Regression | Algorithm used -Linear Regression using OLS

CAN U PREDICT CORRECTLY WHETHER A CUSTOMER WILL CANCEL THE RESERVATION?? You can find the dataset from this kaggle website: https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset

This project involves the use of K-Means Clustering to find the best accommodation for students in Bangalore (or any other city of your choice) by classifying accommodation for incoming students on the basis of their preferences on amenities, budget and proximity to the location.

Trying to predict survival rate of passengers using algorithms like Logistic Regression, Ada Boost, Gradient Boost , Decision Tree Classifiers , Extra Tree Classifiers , Random Forest Classifiers and XG Boost with appropriate data preprocessing techniques.

In this project, I have used logistic regression, a supervised machine learning algorithm, to predict whether a person has diabetes or not based on various features such as age, blood pressure, glucose level, body mass index, etc. I have used Python and popular libraries such as Pandas, Scikit-Learn, and Matplotlib to perfom model building

This is the cumulative repository for the research project Deep Learning Approach to Robotic Prosthetic Wrist Control using EMG Signals done in the AWEAR lab. This repository would consist of all the Data processing pipelines codes, custom data preprocessing library built for this project, and all the time series CNN training Jupyter notebooks using the Data collected within the AWEAR Lab, University at Buffalo.

The data is available in kaggle competitions. https://www.kaggle.com/competitions/spaceship-titanic I have participated and completed the competition on my own.