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Titaaa
Titaaa

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Mar 16

RFM Customer Segmentation SuperStore Dataset

In this article, we will use RFM analysis to segment customers of a fictional superstore in California, which is the state with the most profit. The aim is to understand the purchasing behavior of different customer groups and identify opportunities for targeted marketing campaigns. Dataset: The dataset we will be…

3 min read

RFM Customer Segmentation SuperStore Dataset
RFM Customer Segmentation SuperStore Dataset

3 min read


Mar 7

Sales & KPI Superstore Sales Dashboard

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…

Power Bi

3 min read

Sales & KPI Superstore Sales Dashboard
Sales & KPI Superstore Sales Dashboard
Power Bi

3 min read


Feb 3

Predicting and Retaining At-Risk Telco Customers through Improved Recall

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. …

2 min read

2 min read


Jul 17, 2022

Visualization dashboard COVID-19 with Google DataStudio [Apprenticeship project]

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…

3 min read

Visualization dashboard COVID-19 with Google DataStudio [Apprenticeship project]
Visualization dashboard COVID-19 with Google DataStudio [Apprenticeship project]

3 min read


Jun 25, 2022

IkanMu : Fish Species and Freshness Detection Application Using SSD MobileNet V3

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…

1 min read

IkanMu : Fish Species and Freshness Detection Application Using SSD MobileNet V3
IkanMu : Fish Species and Freshness Detection Application Using SSD MobileNet V3

1 min read


Apr 29, 2022

Deep Q Learning CartPole using Tensorflow Keras .

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 experimentation. I chose Keras because Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy. In contrast, arbitrarily advanced workflows should be possible via a clear path that builds upon what you’ve already learned.

1 min read

Deep Q Learning CartPole using Tensorflow Keras .
Deep Q Learning CartPole using Tensorflow Keras .

1 min read


Apr 28, 2022

Badminton Single Sports Motion Analysis [Academic Project]

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. …

3 min read

Badminton Single Sports Motion Analysis [Academic Project]
Badminton Single Sports Motion Analysis [Academic Project]

3 min read


Apr 4, 2022

Flower classification Fully-Connected Image Classifier [AppertincehipProject ]

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.

1 min read

1 min read

Titaaa

Titaaa

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