{"id":3471,"date":"2023-08-31T11:12:59","date_gmt":"2023-08-31T11:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/car-co2-emissions\/"},"modified":"2025-09-15T16:37:07","modified_gmt":"2025-09-15T14:37:07","slug":"car-co2-emissions","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/car-co2-emissions\/","title":{"rendered":"Estimate car emissions using machine learning"},"content":{"rendered":"\t\t
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In this example, we will build machine learning to estimate car emissions, a topical issue that should raise awareness in the reader.<\/p>\n<\/section>\n

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Nowadays, it is essential to reduce CO2 emissions in the atmosphere to fight against climate change. Indeed, many laws forbid cars whose CO2 emissions are above a specific limit; they must be below it to be approved to drive through the roads. We will predict CO2 emissions with certain independent variables.<\/p>\n<\/section>\n

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Any car company could adjust this value by modifying parameters like the engine’s size and many other engine features.For this study, we have gathered an extensive data set of different types of cars that could exist anywhere in the world.<\/p>\n<\/section>\n

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This example is solved with Neural Designer<\/a>. To follow it step by step, you can use the free trial<\/a>.<\/p>\n<\/section>\n

Contents<\/h3>\n
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  1. \n
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    1. Application type<\/a>.<\/li>\n
    2. Data set<\/a>.<\/li>\n
    3. Neural network<\/a>.<\/li>\n
    4. Training strategy<\/a>.<\/li>\n
    5. Model selection<\/a>.<\/li>\n
    6. Testing analysis<\/a>.<\/li>\n
    7. Model deployment<\/a>.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      <\/p>\n

      1. Application type<\/p>\n

      This is an approximation<\/a> project since the variable to be predicted is continuous (CO2 emissions). The fundamental goal here is to model the CO2 emissions as a function of several car engine features.<\/section>\n
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      2. Data set<\/h2>\n

      The first step is to prepare the data set<\/a>, which is the source of information for the approximation problem. It is composed of:<\/p>\n