I am a dedicated and ambitious Data scientist with a passion for leveraging data-driven insights to solve complex problems. Equipped with a solid foundation in statistical analysis, machine learning, and programming, I strive to extract valuable information from raw data and contribute to data informed decision making processes. With a strong ability to translate business requirements into analytical solutions, I am committed to driving meaningful outcomes through innovative data driven strategies.
Dimas Rahmat Nuriza (dimasrahmatnuriza)
Python PostgreSQL Microsoft Excel Data Analysis Microsoft Power BI Machine Learning Artificial Intelligence Data Science
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| User Name: | dimasrahmatnuriza |
| Account Type: | Personal Account |
| Date Registered: | 02/06/2023 18:37:06 WIB |
| Last Seen: | 27/09/2023 13:09:22 WIB |
| Provinsi: | Jawa Barat |
| Kabupaten: | Kota Bekasi |
| Website: | https://github.com/dimasrahmatnuriza?tab=repositories |
| Online Hours: | 9.24 |
| Projects Won: | 0 |
| Projects Completed: | 0 |
| Completion Rate | - |
| Projects Arbitrated: | 0 |
| Arbitration Rate | - |
| Current Projects: | 0 |
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2022: 1. Data Preprocessing
- Handling Missing Data
- Handling Outlier
- Data Cleaning
- Ffeature Selection
2. EDA ( Exploratory Data Analysis )
- Membuat visualisasi dan mendapat insght dari data yang sudah matang.
3. Hyper Parameter Tuning
4. Modeling
5. Deploy Modeling
- Membuat Aplikasi Sederhana Menggunakan Streamlit
2022: 1. Data Preprocessing
- Handling Missing Data
- Handling Outlier
- Data Cleaning
- Ffeature Selection
2. EDA ( Exploratory Data Analysis )
- Membuat visualisasi dan mendapat insght dari data yang sudah matang.
3. Hyper Parameter Tuning
4. Modeling
5. Deploy Modeling
- Membuat Aplikasi Sederhana Menggunakan Streamlit
2023: 1. Data Preprocessing
- Handling Missing Data
- Handling Outlier
- Data Cleaning
- Ffeature Selection
2. Data Transformation
3. Modeling Menggunakan Kmeans
4. Penentuan Jumlah Cluster
5. Evaluasi Hasil Clustering
6. Visualisasi Hasil Clustering
7. Prediksi Hasil Clustering
8. Deploy Model membuat ( API ) aplikasi sederhana menggunakan streamlit
2023: 1. Data Preprocessing
- Handling Missing Data
- Handling Outlier
- Data Cleaning
- Ffeature Selection
2. EDA ( Exploratory Data Analysis )
- Membuat visualisasi dan mendapat insght dari data yang sudah matang.
3. Class Imbalance
- (Oversampling) manipulasi data mayoritas dan minoritas
4. Hyper Parameter Tuning
5. Modeling
- Evaluasi Modeling
6. Deploy Modeling
- Membuat Aplikasi Sederhana Menggunakan Streamlit
2023: Membuat Dashboard Menggunakan Tableau
2023: Membuat Dashboar Menggunakan Tableau
2023: Membuat Dashbooard Menggunkan Tableau
2023: Membuat API ( Postgre to Python )
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