hillidatulilmi Goto Github PK

belajar-data-science

belajat cara menggunakan Git & Github

dataset

eda-and-deep-learning-ann-telco-customer-churn-predictions

Predict the case of Telco Customer Churn data set as a measure of the percentage of customer accounts that cancel by choosing not to renew their subscription (No) and continue to renew their service (YES). Measured based on actual usage or failure when getting telco service. Based on this, I will analyze churn using the Deep Learning ANN method.

eda-and-deep-learning-cnn-fruit-classification-predictions

Classifying fruit types using a deep learning method, namely Convolutional Neural Network (CNN/ConvNet), which is a type of artificial neural network that is generally used in image recognition and processing. And carry out the process of improvement mode with transfer learning.

eda-and-machine-learning-default-of-credit-card-clients

Show several visualizations in the form query, histograms, bar charts and pie charts and others. In this analysis approach, the target variable is limited or categorical, in the form of YES or NO (binary). Based on this, I will analyze the income group with seven methods Supervised Learning.

eda-and-machine-learning-pima-indians-diabetes-predictions

Show machine learning data set is to predict diagnostically whether a patient has diabetes (Class 1) or does not represent a person with diabetes (Class 0), based on certain diagnostic measures as features included in the data set. The Pima Indians Diabetes dataset consists of several medical parameters of binary values 0 and 1.

hillidatulilmi

Config files for my GitHub profile.

learning-journal-0

Introduction to Data Science, Probability, Statistics & Mathematics. This folder Learn the fundamentals of Python, NumPy, Pandas, and SQL for analyzing data. Draw conclusions from data with probability, descriptive and inferential statistics, and mathematics such as calculus and algebra. Data Visualization to create interactive dashboards.

learning-journal-1

This Forder is about Machine Learning Supervised and Unsupervised Learning models with Feature Engineering, Model Evaluation, and Improvement, as well as Deployment models that have been made.

learning-journal-2

Learn Artificial Neural Network, Computer Vision and Natural Language Processing models with TensorFlow to Big Data Analytics and build a team for real practice and create a final project for a portfolio.

linkedin-skill-assessments-quizzes

Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers

live-code

This folder contains live code files for working on data visualization, hypothesis testing, Supervised and Unsupervised materials.

mission-assigment

This folder contains work assignments with data visualization material, hypothesis testing, Supervised and Unsupervised.

project-milestone

This folder contains project assignments that solve the problem of a case based on a dataset with hypothesis testing, Supervised and Unsupervised material.

storytelling-data-and-hypotesis-testing

Storytelling data about inventory Items with visualization. Statistical analysis of the case of orders items & products with hypothesis testing. Then create a web application that contains the results of the analysis.

streamlit-backend-and-frondend

Creating Backend and frontend deployment models using logistic regression method to predict diabetes