Because of their abundance of amino acids, polysaccharides, and many other nutrients that benefit human beings, mushrooms are deservedly popular as dietary cuisine both worldwide and in China. However, if people eat poisonous fungi by mistake, they may suffer from nausea, vomiting, mental disorder, acute anemia, or even death. Each year in China, there are around 8000 people became sick, and 70 died as a result of eating toxic mushrooms by mistake. It is counted that there are thousands of kinds of mushrooms among which only around 900 types are edible, thus without specialized knowledge, the probability of eating toxic mushrooms by mistake is very high. Most people deem that the only characteristic of poisonous mushrooms is a bright colour, however, some kinds of them do not correspond to this trait. In order to prevent people from eating these poisonous mushrooms, we propose to use deep learning methods to indicate whether a mushroom is toxic through analyzing hundreds of edible and toxic mushrooms smartphone pictures. We crowdsource a mushroom image dataset that contains 250 images of poisonous mushrooms and 200 images of edible mushrooms. The Convolutional Neural Network (CNN) is a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers, which can generate a relatively precise result by analyzing a huge amount of images, and thus is very suitable for our research. The experimental results demonstrate that the proposed model has high credibility and can provide a decision-making basis for the selection of edible fungi, so as to reduce the morbidity and mortality caused by eating poisonous mushrooms. We also open source our hand collected mushroom image dataset so that peer researchers can also deploy their own model to advance poisonous mushroom identification.

In above screenshot, one can see that I have assign k value as 9 which will return the 9 best features and the score function is chi2 and in the last, the scores can be seen for our best 9 features, the highest score is for column'bruises' and the lowest is for column'habitat'. Little Feature Engineering was done i.e. I merged'stalk_surface_above_ring' and'stalk_surface_below_ring' to just'stalk_surface'. Later Multicollinearity was checked using Variance Inflation Factor but our task is just prediction so Multicollinearity issue will not affect . But let me explain little bit more about it.

A mushroom, or toadstool, is the fleshy, spore-bearing fruiting body of a fungus, typically produced above ground on soil or on its food source. In this project, looking at the various properties of a mushroom, we will predict whether the mushroom is edible or poisonous. To be more understandable, let's write properties one by one. Also, following image shows mushroom parts as we mentioned above. Welcome to this project on predict whether mushroom is edible or poisonous in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id.

Just by hearing the names of these dishes, people be drooling! Their flavor is one reason that takes the dish to the next level! But have you ever wondered if the mushroom you eat is healthy for you? From over 14,000 species of mushrooms in the world, how will you classify the mushroom as edible or poisonous? Poisonous mushrooms can be hard to identify in the wild!