Saya adalah seorang Data Scientist dengan keahlian problem solving dan mengoperasikan Python dan R. Saya mampu menganalisis data dengan efisien, mengidentifikasi tren, dan memberikan wawasan berharga untuk tujuan bisnis. Keterampilan teknis saya dalam kedua bahasa pemrograman ini memungkinkan saya untuk mengimplementasikan berbagai algoritma dan metode analisis data dengan baik, menjadikan saya aset berharga bagi setiap organisasi yang mengandalkan data untuk pengambilan keputusan strategis. Selain itu, saya juga telah memiliki sertifikat BNSP Associate Data Scientist, yang menegaskan kemampuan dan kompetensi saya dalam bidang ini.
daffa28 (daffa28)
Data Entry Python Flask Python Data Analysis Data Mining Visual Studio Code Data Science
Hire Me
Make a Private Project
Invite to Bid
Existing Projects
Make a Private Project
Invite to Bid
Existing Projects
| User Name: | daffa28 |
| Account Type: | Personal Account |
| Date Registered: | 27/09/2024 11:53:58 WIB |
| Last Seen: | 02/10/2024 12:09:37 WIB |
| Provinsi: | Jawa Barat |
| Kabupaten: | Kota Bekasi |
| Website: | dazzerz.github.io |
| Online Hours: | 1.01 |
| Projects Won: | 0 |
| Projects Completed: | 0 |
| Completion Rate | - |
| Projects Arbitrated: | 0 |
| Arbitration Rate | - |
| Current Projects: | 0 |
Ratings & Rankings
As Worker
0.00/10.00
0 Point
No Ranking
0 Projects
0.00/10.00
0 Point
No Ranking
0 Projects
As Owner
0.00/10.00
0 Point
No Ranking
0 Projects
0.00/10.00
0 Point
No Ranking
0 Projects
As Seller
0.00/10.00
0 Point
No Ranking
0 Sales
0.00/10.00
0 Point
No Ranking
0 Sales
As Affiliate
0.00/10.00
0 Point
No Ranking
0 Users
0.00/10.00
0 Point
No Ranking
0 Users
Services
No record found.
Products
No record found.
2024: Wine Classification
Logistic Regression models show stable performance with training accuracies between 0.7320-0.7339 and test accuracies between 0.7215-0.7277. Feature selection and SMOTE offer minimal accuracy improvements.
2024: Tweet Disaster Classification
The Logistic Regression model achieves 80.12% training accuracy, with high recall (82.1%) but lower precision (68.6%). The F1 Score of 0.7475 reflects a balanced trade-off between precision and recall.
2024: Data Clustering
Clustering analysis identifies distinct product labeling color preferences: Younger, lower-income individuals prefer pink; medium-income prefer black; higher-income prefer red. Adults prefer blue (low-income), green (medium-income), yellow (high-income). Older individuals prefer red (medium-income) and gray (high-income).
2024: Market Basket Analysis
The project uncovers strong product associations with high confidence and lift values, notably between Citata and Sprit/Sri Roti, and Indomi/Aquos with Sri Roti/Chiki. These insights enable targeted promotions and strategic placement to boost sales.
2024: Data Viz of Cars
A car sales graph using Tableau shows trends and sales volume over time, helping to visualize fluctuations, compare different models, and assess the impact of seasonal or economic factors on demand. It also aids in forecasting future sales and planning strategies.
2024: Data Viz of Lazada
A graph of electronic brand sales on Lazada shows trends and sales volume over time, helping to visualize fluctuations, compare different brands, and assess the impact of seasonal or economic factors on demand. It also aids in forecasting future sales and planning strategies.
No record found.
No record found.
Anda harus login terlebih dahulu untuk melihat data ini.
You must login first to see this data.
Loading ...
