Wise on Weather PDF Download
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Author: Vicki Stracensky
Publisher:
ISBN: 9780692389225
Category :
Languages : en
Pages : 32
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Book Description
"Wise on Weather!" is like getting three books in one:a poem, seasonal safety guide and definitions to 100 weather words - both educational and entertaining. This fun and informative book will hold children's interests for years to come, fostering great learning and enjoyable family time.
Author: Vicki Stracensky
Publisher:
ISBN: 9780692389225
Category :
Languages : en
Pages : 32
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Book Description
"Wise on Weather!" is like getting three books in one:a poem, seasonal safety guide and definitions to 100 weather words - both educational and entertaining. This fun and informative book will hold children's interests for years to come, fostering great learning and enjoyable family time.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 196
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Book Description
In this project, we embarked on a journey of exploring time-series weather data and performing forecasting and prediction using Python. The objective was to gain insights into the dataset, visualize feature distributions, analyze year-wise and month-wise patterns, apply ARIMA regression to forecast temperature, and utilize machine learning models to predict weather conditions. Let's delve into each step of the process. To begin, we started by exploring the dataset, which contained historical weather data. We examined the structure and content of the dataset to understand its variables, such as temperature, humidity, wind speed, and weather conditions. Understanding the dataset is crucial for effective analysis and modeling. Next, we visualized the distributions of different features. By creating histograms, box plots, and density plots, we gained insights into the range, central tendency, and variability of the variables. These visualizations allowed us to identify any outliers, skewed distributions, or patterns within the data. Moving on, we explored the dataset's temporal aspects by analyzing year-wise and month-wise distributions. This involved aggregating the data based on years and months and visualizing the trends over time. By examining these patterns, we could observe any long-term or seasonal variations in the weather variables. After gaining a comprehensive understanding of the dataset, we proceeded to apply ARIMA regression for temperature forecasting. ARIMA (Autoregressive Integrated Moving Average) is a powerful technique for time-series analysis. By fitting an ARIMA model to the temperature data, we were able to make predictions and assess the model's accuracy in capturing the underlying patterns. In addition to temperature forecasting, we aimed to predict weather conditions using machine learning models. We employed various classification algorithms such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), and Multi-Layer Perceptron (MLP). These models were trained on the historical weather data, with weather conditions as the target variable. To evaluate the performance of the machine learning models, we utilized several metrics: accuracy, precision, recall, and F1 score. Accuracy measures the overall correctness of the predictions, while precision quantifies the proportion of true positive predictions out of all positive predictions. Recall, also known as sensitivity, measures the ability to identify true positives, and F1 score combines precision and recall into a single metric. Throughout the process, we emphasized the importance of data preprocessing, including handling missing values, scaling features, and splitting the dataset into training and testing sets. Preprocessing ensures the data is in a suitable format for analysis and modeling, and it helps prevent biases or inconsistencies in the results. By following this step-by-step approach, we were able to gain insights into the dataset, visualize feature distributions, analyze temporal patterns, forecast temperature using ARIMA regression, and predict weather conditions using machine learning models. The evaluation metrics provided a comprehensive assessment of the models' performance in capturing the weather conditions accurately. In conclusion, this project demonstrated the power of Python in time-series weather forecasting and prediction. Through data exploration, visualization, regression analysis, and machine learning modeling, we obtained valuable insights and accurate predictions regarding temperature and weather conditions. This knowledge can be applied in various domains such as agriculture, transportation, and urban planning, enabling better decision-making based on weather forecasts.
Author: S. S. Bassler
Publisher:
ISBN:
Category : Weather
Languages : en
Pages : 106
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Book Description
Author: Helen Cox Cannons
Publisher: Capstone
ISBN: 1484653343
Category : Juvenile Nonfiction
Languages : en
Pages : 28
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Book Description
Through stunning photographs and simple text, books in this series introduce children to different types of weather. In Thunder and Lightning, children learn about different types of lightning, what thunder and lightning are, what causes lightning, and how to stay safe when thunderstorms occur.
Author: Ellen Lawrence
Publisher: Bearport Publishing
ISBN: 1617724041
Category : Juvenile Nonfiction
Languages : en
Pages : 29
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Book Description
Explores clouds, discusses what they are made of, and describes the different types.
Author: Sharon Parsons
Publisher: Nelson Thornes
ISBN: 0748757783
Category : Reading (Elementary)
Languages : en
Pages : 132
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Book Description
With a balance of fiction and non-fiction text types and genres, Bookwise is carefully graded and organised into five cross-curricular strands, encouraging links to other subjects. The full-colour readers are accompanied by Teacher's Guides and Resource Sheets to help you get the most out of your Guided Reading and Writing sessions.
Author:
Publisher:
ISBN:
Category : Marine meteorology
Languages : en
Pages : 514
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Book Description
Author: Mel Goldstein
Publisher: Penguin
ISBN: 9780028643410
Category : Science
Languages : en
Pages : 420
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Book Description
Explains how to track weather patterns, read weather maps, and identify cloud formations while exploring the effects of pollution, hurricanes, and El Niäno.
Author: Sue Reed
Publisher: New Society Publishers
ISBN: 1550926802
Category : Gardening
Languages : en
Pages : 322
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Book Description
What can we do, right now, in our own landscapes, to help solve climate change? Predictions about future effects of climate change range from mild to dire - but we're already seeing warmer winters, hotter summers, and more extreme storms. Proposed solutions often seem expensive and complex, and can leave us as individuals at a loss, wondering what, if anything, can be done. Sue Reed and Ginny Stibolt offer a rallying cry in response - instead of wringing our hands, let's roll up our sleeves. Based on decades of experience, this book is packed with simple, practical steps anyone can take to beautify any landscape or garden, while helping protect the planet and the species that call it home. Topics include: Working actively to shrink our carbon footprint through mindful landscaping and gardening Creating cleaner air and water Increasing physical comfort during hotter seasons Supporting birds, butterflies, pollinators, and other wildlife. This book is the ideal tool for homeowners, gardeners, and landscape professionals who want to be part of the solution to climate change. AWARDS GOLD | 2018 Nautilus Book Awards: Ecology & Environment
Author: Robert Hunter
Publisher:
ISBN:
Category : English language
Languages : en
Pages : 1268
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Book Description
