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Mushrooms are a special kind of food, they are very unique because of their edibility. Most countries consider mushrooms as a high content nutritious food. A mushroom, also called as a toadstool, is a fleshy, spore-bearing fruiting body of a fungus, mostly produced above ground level on soil or on any of its food source. The term mushroom can also be defined as a collection of other fungi, which may or may not contain stems. Therefore mushroom is used to describe the fruiting body parts which is fleshy. The gills of mushrooms erupts some microscopic spores which help the fungus to spread across the ground surface. To classify or to identify a mushroom we require a basic knowledge of their macroscopic patterns. Most of them Basidiomycetes and gilled. The color of the powdery print from the spores of mushroom, which is called a spore print is used to help classify mushrooms and can help to identify them. Spore print colors may include white (which is most common), brown, black, purple-brown, pink, yellow, and creamy, but almost never blue, green, or red.
Nowadays Mushrooms are used extensively in cooking, in almost all parts of the country. But separating edible from poisonous species requires tedious attention to be observed. Usually, there is no specific trait by which all toxic mushrooms can be identified, nor the edible mushrooms. Many mushrooms may produce metabolites that can make it toxic. Toxicity likely plays an important thing to be considered or analyzed.
Why: Problem statement
Mushrooms on top of pizza add a great taste! But with over 10000 species of mushrooms are available in the market today, how can we differentiate it and predict which ones are edible? Usually, only small portions of mushrooms are edible. It is really risking to eat or consume a poisonous mushroom. Hence, in this project, it is decided to use some classification algorithms and codes to develop the best model to analyze if mushrooms are edible based on the available data of the mushrooms. Also, it adds an opportunity to study and compare the classifiers and also understand how they function.
Each species of mushrooms are identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended.
In this project, we will explore and analyze the data presented by "UCI Machine Learning" for mushroom classification. The aim of this dataset is to classify between edible to poisonous mushrooms. First, we shall analyze the data, by collecting its available characteristics, and then we will classify it to achieve maximum accuracy and precision.
How: Solution description
Some mushrooms contain less toxic compounds and, therefore, are not severely poisonous. Poisonings by these mushrooms may respond well to treatment. However, certain types of mushrooms, contain very potent toxins and are very poisonous; so even if symptoms are treated promptly, mortality is high. With some toxins, death can occur in a week or a few days. Although a liver or kidney transplant may save some patients with complete organ failure, in many cases there are no organs available. Patients hospitalized and given aggressive support therapy almost immediately after ingestion of amanitin-containing mushrooms have a mortality rate of only 10%, whereas those admitted 60 or more hours after ingestion have a 50–90% mortality rate.
In this one, we'll look at how we can create a machine learning model, an artificial neural network ( ANN), to do classification predictions on a data set.
Artificial neural networks (ANN):
ANN or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.
How is it different from competition
Obviously, every coding will be done in Python and Anaconda. It is decided to use the model to attempt to predict whether or not a mushroom is edible or poisonous based on the training data set. It predicts the response variable correctly. Suppose if you receive thousands of rows of data with dozens of columns about mushroom characters, will you be able to identify which characteristics make a mushroom edible or poisonous? By the end of this project, you will be able to answer the following Would you trust your model? Will it be enough for you to make a decision on whether or not to eat a mushroom you find?
Who are your customers
The general public people, families, everyone who eats mushrooms are the customer of this project.