Programming Languages:python
framework-FastAPI/Flask
Library -pymongo,requests
Data Collection and Cleaning:BeautifulSoup, Scrapy,Pandas
Data Storage:MongoDB
Data Visualization:Matplotlib, Seaborn,Plotly
Machine Learning or Predictive Modelling:Scikit-learn, TensorFlow,pyTorch
ML algorithms : Regression
Implementation
Here,Data Collection and Cleaning should be done first. For that,past Waste generation data should be collected. Various data such as historical records,loT sensors,urban data,holiday schedules, weather conditions and population density databases are used. To efficiently access and retrieve this information, Pythons versatile libraries such asrequests` are used. Pandas are used to preprocess and clean the above data.
A MongoDB database is used to store the above cleaned and pre-processed data so that the data can be managed efficiently. Pythonspymongo` library handles establishing connections and storing structured data in MongoDB.
Ploty, a data visualisation library in Python, is used to create visualisation from the collected and processed data. Interactive plots and graphs are used to explore patterns associated with waste generation trends in relation to various factors such as historical records, loT sensors, or municipal data, holiday schedules, weather conditions, and population density. Interactive plots and graphs are important for understanding relationships in data and creating predictive models.
PyTorch, a versatile machine learning library in Python, is used to run regression models to predict waste generation. Models such as Linear Regression or Neural Networks are used here. These models are trained on the prepared training dataset to learn patterns and relationships.
And the performance of the test dataset model is evaluated using evaluation metrics such as Mean Squared Error (MSE) , Root Mean Squared Error (RMSE) or R-squared. The robustness of the model is ascertained using cross-validation techniques to ensure that it can generalise well to unobserved data.
Once the model shows good performance, frameworks like Flask or FastAPI are used to seamlessly integrate it and use it as an API. this enables waste collectors to access forecasts and alerts based on waste generation and facilitate more efficient waste management.
Tools
Programming Languages:python framework-FastAPI/Flask Library -pymongo,requests Data Collection and Cleaning:BeautifulSoup, Scrapy,Pandas Data Storage:MongoDB Data Visualization:Matplotlib, Seaborn,Plotly Machine Learning or Predictive Modelling:Scikit-learn, TensorFlow,pyTorch ML algorithms : Regression
Implementation
Here,Data Collection and Cleaning should be done first. For that,past Waste generation data should be collected. Various data such as historical records,loT sensors,urban data,holiday schedules, weather conditions and population density databases are used. To efficiently access and retrieve this information, Python
s versatile libraries such asrequests` are used. Pandas are used to preprocess and clean the above data.A MongoDB database is used to store the above cleaned and pre-processed data so that the data can be managed efficiently. Python
spymongo` library handles establishing connections and storing structured data in MongoDB.Ploty, a data visualisation library in Python, is used to create visualisation from the collected and processed data. Interactive plots and graphs are used to explore patterns associated with waste generation trends in relation to various factors such as historical records, loT sensors, or municipal data, holiday schedules, weather conditions, and population density. Interactive plots and graphs are important for understanding relationships in data and creating predictive models.
PyTorch, a versatile machine learning library in Python, is used to run regression models to predict waste generation. Models such as Linear Regression or Neural Networks are used here. These models are trained on the prepared training dataset to learn patterns and relationships.
And the performance of the test dataset model is evaluated using evaluation metrics such as Mean Squared Error (MSE) , Root Mean Squared Error (RMSE) or R-squared. The robustness of the model is ascertained using cross-validation techniques to ensure that it can generalise well to unobserved data.
Once the model shows good performance, frameworks like Flask or FastAPI are used to seamlessly integrate it and use it as an API. this enables waste collectors to access forecasts and alerts based on waste generation and facilitate more efficient waste management.