https://github.com/serverdaun/ml_project_sber
This repository contains a machine learning project aimed at predicting user actions on a car subscription service’s website.
https://github.com/serverdaun/ml_project_sber
classification-algorithm fastapi machine-learning predictive-modeling python
Last synced: 30 days ago
JSON representation
This repository contains a machine learning project aimed at predicting user actions on a car subscription service’s website.
- Host: GitHub
- URL: https://github.com/serverdaun/ml_project_sber
- Owner: serverdaun
- Created: 2024-04-21T19:21:00.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-10T21:50:44.000Z (over 1 year ago)
- Last Synced: 2025-01-10T00:22:00.661Z (over 1 year ago)
- Topics: classification-algorithm, fastapi, machine-learning, predictive-modeling, python
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ML Project: Sber Auto Subscription
## Table of Contents
1. [Introduction](#introduction)
2. [Project Structure](#project-structure)
3. [Installation](#installation)
4. [Usage](#usage)
## Introduction
This project involves developing a machine learning model for a car subscription service.
The main task is to predict whether a user will perform a target action on the company's website.
Key aspects of the project include:
1. Working with Google Analytics data to analyze user behavior on the website.
2. Developing a predictive model to determine if a user will perform a target action (e.g., submitting an application or requesting a call).
3. The goal is to achieve a ROC-AUC score of approximately 0.65.
4. Creating a service that takes input features such as UTM parameters, device information, and geo data, and outputs a binary prediction (0 or 1) indicating whether the user is likely to perform a target action.
5. The project requires data preprocessing, feature engineering, model selection, and API implementation.
## Project Structure
- **data/skillbox_diploma_main_dataset_sberautopodpiska** # Contains two datasets: information about sessions and information about hits
- **notebooks/** # Jupyter notebooks for data preprocessing, EDA, feature engineering, and modeling
- **model/** # Directory for model files:
- **pipeline.py** # Steps for model creation
- **model.pkl** # Final trained model
- **main.py** # FastAPI app configuration
- **requirements.txt** # Python dependencies
- **README.md** Project documentation
## Installation
1. Clone the repository
```sh
git@github.com:serverdaun/ml_project_sber.git
cd ml_project_sber
```
2. Create and activate a virtual environment
```sh
python3 -m venv venv
source venv/bin/activate
```
3. Install the required dependencies
```sh
pip install -r requirements.txt
```
## Usage
1. Start the FastAPI application to serve the model and make predictions through a REST API.
```sh
uvicorn main:app --reload
```
2. Once the server is running, you can use tools like 'curl' or Postman to make requests to the API. Below is the list
of the calls.
- GET
- 'http://127.0.0.1:8000/status' to get the status of the app
- 'http://127.0.0.1:8000/version' to get model metadata
- POST
- 'http://127.0.0.1:8000/predict' to make predictions with json format data in body
3. Example data for API
```sh
{
"session_id": "9055434745589932991.1637753792.1637753792",
"client_id": "2108382700.1637753791",
"visit_date": "2021-11-24",
"visit_time": "14:36:32",
"visit_nuber": 1,
"utm_source": "ZpYIoDJMcFzVoPFsHGJL",
"utm_medium": "banner",
"utm_campaign": "LEoPHuyFvzoNfnzGgfcd",
"utm_adcontent": "vCIpmpaGBnIQhyYNkXqp",
"utm_keyword": "puhZPIYqKXeFPaUviSjo",
"device_category": "mobile",
"device_os": "Android",
"device_brand": "Huawei",
"device_model": "example_model",
"device_screen_resolution": "360x720",
"device_browser": "Chrome",
"geo_country": "Russia",
"geo_city": "Zlatoust"
}
```