[vc_row row_type=”3″ blox_image=”21394″ align_center=”aligncenter” page_title=”page-title-x” blox_dark=”true” parallax_speed=”8″][vc_column]

---

---

[vc_column_text]

Billing risks evaluation

[/vc_column_text]

---

---

[/vc_column][/vc_row][vc_row][vc_column width=”1/4″]

---

---

[/vc_column][vc_column width=”1/2″]

---

[vc_column_text]The project was implemented for an international energy company based in Germany.[/vc_column_text]

---

[/vc_column][vc_column width=”1/4″][/vc_column][/vc_row][vc_row][vc_column width=”1/3″][vc_single_image image=”21769″ img_size=”550×350″][/vc_column][vc_column width=”1/3″][vc_single_image image=”21397″ img_size=”550×350″][/vc_column][vc_column width=”1/3″][vc_single_image image=”21698″ img_size=”550×350″][/vc_column][/vc_row][vc_row][vc_column]

---

[vc_column_text]

About this project

[/vc_column_text][vc_row_inner][vc_column_inner width=”1/2″][vc_column_text]

Project description:

The client owns a large power transmission network including power stations and power transmission towers. The goal of the project was to develop the prediction model for calculating the risks of payment according to the billing information of power transmission tower users. Such a model helped the client in planning the budget and early notification of clients who potentially have long due delays in payment of the bills.

[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text]

Technologies:

• AWS, Python, Python SciKit Learn (shap, xgboost, statsmodels)

[/vc_column_text][/vc_column_inner][/vc_row_inner]

---

[/vc_column][/vc_row]