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Impact of climate change on Developing economies

Climate change is having a significant impact on trade in developing countries. As global temperatures rise and weather patterns become more extreme, these countries are facing increased challenges in terms of crop yields, natural resource availability, and infrastructure resilience. Further, as the more advanced economies introduce strategies to reduce their carbon emissions and achieve their net zero targets, this will put unequal pressure on the trading capacities of developing countries. The impact of climate change on economic growth in developing countries is expected to be significant and negative. Developing countries are generally more vulnerable to the impacts of climate change than developed countries due to a number of factors, including: Greater reliance on agriculture and natural resources. Many developing countries have economies that are heavily reliant on agriculture and natural resources, which are sectors that are particularly vulnerable to climate change...
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How to Reduce my Carbon Footprint?

Eating, travelling, heating your home… What's the carbon footprint of these activities and how can we make more climate-friendly choices? When you drive your car, buy a pair of sneakers or grill a steak, you contribute to the emission of carbon dioxide and other greenhouse gases into the atmosphere. It’s your carbon footprint. Many countries, institutions and companies have committed to reduce their emissions. As an individual, you can also estimate your carbon footprint and reduce it. Discover how. What is a carbon footprint? Greenhouse gases are emitted through the production and consumption of goods and services. Carbon footprint is a concept used to quantify the impact of an activity, a person or a country on climate change. How much carbon is emitted to produce your t-shirt, meal or phone? The amount will depend on production and consumption choices. If we take the example of transport, taking the plane emits 285g of carbon per kilometre, compared to 104g for a car and 14g...
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Eco Bricks

What can be done with plastic that you daily put into your dustbins? P lastic is something has changed our habits to switch to more non renewable stuffs from renewable sources. But the question here is what makes plastic more suitable for carrying any household activities. We see in our home as well,we tend to choose plastics over clothing bags. But what made this transition? Well it turns out to be: Light weight: plastic packaging is lightweight and can take up less space than alternatives, which means lighter loads for planes and trucks and lower emissions. Durable: because plastic packaging is so durable, plastic packaging can be very thin. … Versatile: plastics can be transformed in many different ways. Although these are advantages of plastic but do you know plastic is a major cause of climate change. Then why to choose products which are useful but eventually harm our environment and indirectly us.Most of the plastic waste which we deposit in the soil either don’t degrade qu...
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Python Libraries used in Astronomy

Machine learning libraries in astronomy are software libraries that provide tools and functions for building and implementing machine learning models to analyze astronomical data. These libraries typically include a variety of features, such as: Data loaders: These loaders can read in astronomical data from a variety of formats, such as FITS, HDF5, and CSV. Preprocessing functions: These functions can be used to clean and prepare astronomical data for machine learning. Machine learning algorithms: These algorithms can be used to train and evaluate machine learning models for a variety of tasks, such as classification, regression, and clustering. Visualization tools: These tools can be used to visualize the results of machine learning models. Here are some of the most popular machine learning libraries in astronomy: AstroML: AstroML is a Python library for machine learning and data mining in astronomy. It provides a variety of tools and functions for analyzing ast...
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AI in Astronomy

Artificial intelligence (AI) is helping astronomers in a number of ways, including: Analyzing vast amounts of data: Astronomy produces enormous amounts of data, from images and spectra to light curves and radial velocity measurements. AI algorithms can quickly and accurately analyze this data, identifying patterns and trends that would be difficult or impossible for humans to find on their own. Automating tasks: AI can be used to automate many of the repetitive tasks involved in astronomical research, such as identifying and classifying objects in images, measuring their properties, and extracting data from spectra. This frees up astronomers to focus on more creative and strategic work. Making new discoveries: AI is helping astronomers to make new discoveries in a number of areas, including the search for exoplanets, the study of dark matter and dark energy, and the understanding of the early universe. For example, AI algorithms have been used to identify new exop...
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Feature Scaling In Machine Learning!

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    Feature Scaling is a technique of bringing down the values of all the independent features of our dataset on the same scale . Feature scaling helps to do calculations in algorithms very quickly. It is the important stage of data preprocessing. If we don't do feature scaling then the machine learning model gives higher weightage to higher values and lower weightage to lower values. Also, takes a lot of time for training the machine learning model.After doing feature scaling, we can conveniently train our models and draw predictions. Types of Feature Scaling Normalization Normalization is a scaling technique in which the values are rescaled between the range 0 to 1 . To normalize our data, we need to import MinMaxScalar from the Sci-Kit learn library and apply it to our dataset. After applying the MinMaxScalar, the minimum value will be zero and the maximum value will be one. 2. Standardization Standardization is another scaling technique in which the mean will be eq...
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                                                          Ensemble Models   T he Ensemble technique is one of the most fundamental algorithms for classification and regression in the Machine Learning world. In the election, we know that the candidate would win when they get a maximum number of votes i.e majority of votes. The Ensemble technique has a similar underlying formula where we aggregate predictions from a group of predictors (models), which may be classifiers or regressors, and most of the time the prediction is better than the single predictor. Such algorithms are called Ensemble methods and such predictors are called Ensembles. The Ensemble technique is a combination of multiple models , ...
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