https://github.com/sushantdhumak/traffic-forecasting-using-iot-sensor-data
Demonstrates how to utilize XGBoost for traffic forecasting using data gathered from IoT sensors, highlighting its efficiency in processing complex datasets and delivering accurate predictions.
https://github.com/sushantdhumak/traffic-forecasting-using-iot-sensor-data
data-analysis data-visualization exploratory-data-analysis feature-engineering feature-importance feature-selection gridsearchcv hyperparameter-optimization hyperparameter-tuning iot random-search xgboost-regression
Last synced: 6 months ago
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Demonstrates how to utilize XGBoost for traffic forecasting using data gathered from IoT sensors, highlighting its efficiency in processing complex datasets and delivering accurate predictions.
- Host: GitHub
- URL: https://github.com/sushantdhumak/traffic-forecasting-using-iot-sensor-data
- Owner: sushantdhumak
- Created: 2025-01-03T08:27:25.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-03T08:32:34.000Z (9 months ago)
- Last Synced: 2025-01-30T23:05:56.918Z (8 months ago)
- Topics: data-analysis, data-visualization, exploratory-data-analysis, feature-engineering, feature-importance, feature-selection, gridsearchcv, hyperparameter-optimization, hyperparameter-tuning, iot, random-search, xgboost-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 908 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
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### **Traffic Forecasting using IoT Sensor Data**
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#### **Context**
You are working with the government to transform your city into a smart city. The vision is to convert it into a digital and intelligent city to improve the efficiency of services for the citizens. One of the problems faced by the government is traffic. You are a data scientist working to manage the traffic of the city better and to provide input on infrastructure planning for the future.
The government wants to implement a robust traffic system for the city by being prepared for traffic peaks. They want to understand the traffic patterns of the four junctions of the city. Traffic patterns on holidays, as well as on various other occasions during the year, differ from normal working days. This is important to take into account for your forecasting.
#### **Content**
To predict traffic patterns in each of these four junctions for the next 4 months.
The sensors on each of these junctions were collecting data at different times, hence you will see traffic data from different time periods. To add to the complexity, some of the junctions have provided limited or sparse data requiring thoughtfulness when creating future projections. Depending upon the historical data of 20 months, the government is looking to you to deliver accurate traffic projections for the coming four months. Your algorithm will become the foundation of a larger transformation to make your city smart and intelligent.
Dataset Link:
https://www.kaggle.com/datasets/vetrirah/ml-iot/data
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#### **Variable Information**
**ID** : Unique IDs for each reading
**DateTime** : Date and Time in hourly interval
**Junction** : Junction Number (Observation collected)
**Vehicles** : Number of Vehicles (Target)
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#### **Project Objective**
Demonstrates how to utilize XGBoost for traffic forecasting using data gathered from IoT sensors, highlighting its efficiency in processing complex datasets and delivering accurate predictions.