The purpose of this project is to provide a comprehensive view of our sales performance and key performance indicators (KPIs) in one central location. With this dashboard, we can easily track the growth of monthly orders and sales trends by segment and category. In addition to these metrics, the KPI…

In the fast-paced world of telecommunications, retaining customers is critical to the success of any company. One of the biggest challenges faced by companies is predicting which customers are at risk of churning, or leaving the company. This is where the Telco Churn project comes into play. …

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. In this work, i try to learn to visualize a spreading virus that has spread the country’s 34 provinces…

Final Project at Orbit Future Academy , : Ikanmu mobile applicatio The datasets used in this final project are images of fish obtained by taking approximately 300 images of each type of fish taken at TPI (Fish Auction Place) Cilacap and datasets from websites that provide fish datasets as additional…

This project aims to introduce the concept of Deel Q Learning and use it to solve the CartPole environment from the OpenAI Gym. Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast…

Badminton is a national sport in several Asian countries, a sport that originated in China and was found in England. Badminton is a game using a racket that two to four people playwith a different temporal structure defined by the high-intensity movement of short duration. …

I will train a fully-connected neural network to image classification of dandelions and grasses. I will be using the TensorFlow Deep Learning Framework to create a neural network and training/validation dataset. https://github.com/titax137/repo-orbit-tita/blob/head/Flower_classification_Fully_Connected_Image_Classifier.ipynb Conclusion: The Neural Network model using CNN has a higher accuracy rate of 0.6688, while the previous neural network model has a lower accuracy level of 0.611.