Open source project for data preparation of LLM application builders

sciblox - Easier Data Science and Machine Learning

Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.

EverAnalyzer is my thesis in the Department of Digital Systems of the University of Piraeus. EverAnalyzer is a platform for collecting, preprocessing, processing and analyzing Big Data from the Twitter platform.

This project creates a statistical model to predict demand for loans in each region of the USA based on monthly family income and rental costs. The results are displayed on a dashboard updated periodically with data retrieval.

My side project about Data Scientist

Model for easy facilitation of visa processing and approvals

Machine Learning Engineer Nanodegree, Supervised Learning, Finding Donors for CharityML

EDA & Data Preprocessing on Google App Store Rating Dataset.

Ini merupakan repositori proyek akhir untuk aplikasi LeafNet yang di buat oleh Tim Ampera dari kelas Asimo Kelompok 3 pada program Artificial Intelligence Mastery Program yang di selenggarakan oleh Orbit Future Academy

Finding Donor for CharityML - Machine Learning Nanodegree from Udacity

Based on the powerful econometrics and statistical background and rich data science resources of School of Economics (SOE) and Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, WISER CLUB is a data science mutual aid learning organization jointly organized by SOE and WISE graduate students and undergraduate students.

Data preparation on raw data using Python

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds

3rd Project for the Post Graduate Programme in Data Science and Business Analytics at the University of Texas at Austin - Linear Regression & Data Preprocessing

In this project, I developed a basic model to predict house prices based on a single feature: the area of the property. Due to the simplicity of the dataset, I chose a linear regression algorithm for prediction. After training the model, I saved the parameters in a pickle file and deployed the model to a web application using Flask.

Final project for DSCI 100: Developed a KNN classification model in R to predict wine quality using physicochemical properties. Conducted data preprocessing, feature selection, and cross-validation to evaluate model performance.

This project focuses on predicting customer purchase behavior using machine learning models, with an emphasis on feature importance.

In this two cluster approaches are used: hierarchical clustering and K-means clustering. It is unsupervised learning technique for grouping related data points which shows same behaviour in the dataset regardless of the outcome.