Found 31 repositories(showing 30)
siddhantborse
Atmos Viz is a Python-based project designed to analyze, visualize, and predict global temperature trends across various cities and countries using time-series analysis and advanced data science techniques. Leveraging historical climate data, this project integrates machine learning models, geospatial mapping, and interactive visualizations to unco
themegashark
No description available
DannyMichaels
Weather App using NextJS and StepZen
This is a working project for CMIP6 Hackathon. This project is proposed by Lorenzo Polvani. The main focus is developing tools to detect atmospheric temperature trends under increasing CO2: contrasting CMIP5 and CMIP6 models
No description available
Danijel-De-Paris
No description available
Nisha2357k
No description available
Nisha2357k
No description available
kentbourgoing
This project analyzed 40 years of CO₂ data to forecast climate thresholds. Understanding CO₂ trends is essential for climate policy and planning. By building forecasting models from a 1997 perspective and validating them against actual data through 2024, the project revealed important insights about model performance and accelerating CO₂ growth.
Nisha2357k
No description available
holy-angel-university
Analyzes CO2 data (1958-2024) from Mauna Loa, Hawaii, revealing a steady increase. Visualizations show upward trends and rising CO2 levels by decade. Machine learning models predict future levels, with Random Forest performing best. Findings highlight urgency for action to curb CO2 emissions and protect our environment.
This dashboard shows key weather trends and conditions, including average temperature (25.45 °C), foggy hours, rainy hours, wind speed, humidity, and visibility. It visualizes monthly temperature changes, humidity vs temperature, and the distribution of weather types, with haze being most frequent
No description available
selinekarabulut
Time Series Analysis
No description available
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Analyst-Femi-Ola-Joseph
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
No description available
Understand the dynamic of global CO2 levels begins with a time series plot showcasing average CO2 concentration over the years.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
No description available
No description available
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016
This project analyzes CO₂ trends from 1958–2016 using Mauna Loa data, revealing seasonal and annual patterns. It shows a clear rise in CO₂ due to human activity, aiming to inform public debate, support advocacy, and guide climate policy through data-driven insights.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.
This data science research project investigates the long-term trends in atmospheric carbon dioxide (CO₂) concentrations from 1958 to 2016.