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It's simple. You are given a set of features extracted from the shape of the beans in images and it's required to predict the type of each bean. There are 7 bean types in this dataset

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mahmoudsoroor/Dry-Beans-Classification-Competition

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Dry-Beans-Classification-Competition

This is For [InClass Prediction Competition]([https://www.kaggle.com/competitions/dry-beans-classification-iti-ai-pro-intake02/overview]


description

  • the goal was to predict predict the type of each bean out of 7 bean types in this dataset.
  • You are provided a set of features extracted from the shape of the beans in images

File descriptions

  • train.csv - the training set.
  • test.csv - the test set.
  • sample_submission.csv - a sample submission file in the correct format

Data fields

  • ID - an ID for this instance
  • Area - (A), The area of a bean zone and the number of pixels within its boundaries.
  • Perimeter - (P), Bean circumference is defined as the length of its border.
  • MajorAxisLength - (L), The distance between the ends of the longest line that can be drawn from a bean.
  • MinorAxisLength - (l), The longest line that can be drawn from the bean while standing perpendicular to the main axis.
  • AspectRatio - (K), Defines the relationship between L and l.
  • Eccentricity - (Ec), Eccentricity of the ellipse having the same moments as the region.
  • ConvexArea - (C), Number of pixels in the smallest convex polygon that can contain the area of a bean seed.
  • EquivDiameter - (Ed), The diameter of a circle having the same area as a bean seed area.
  • Extent - (Ex), The ratio of the pixels in the bounding box to the bean area.
  • Solidity - (S), Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.
  • Roundness - (R), Calculated with the following formula: (4piA)/(P^2)
  • Compactness - (CO), Measures the roundness of an object: Ed/L
  • ShapeFactor1 - (SF1)
  • ShapeFactor2 - (SF2)
  • ShapeFactor3 - (SF3)
  • ShapeFactor4 - (SF4)
  • y - the class of the bean. It can be any of BARBUNYA, SIRA, HOROZ, DERMASON, CALI, BOMBAY, and SEKER.

Technologies and algorithms used

algorithms

  • Support vector classification
  • Multilayer perceptron
  • XGB classifier
  • Voting classifier

Training The Model

Trained using Kaggle CPU


final Score

  • votingclassifier f1_score Train: 0.9502
  • votingclassifier f1_score test: 0.94003

Thanks to my team (Rumbling is Coming)

  • HossamEldin Mohammed,
  • Ahmed Abdel-Rahim El-Sayed,
  • Abdelrhman Elsaied,
  • mahmoud sorour ragab,
  • Muhamed Mustafa

Final result

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It's simple. You are given a set of features extracted from the shape of the beans in images and it's required to predict the type of each bean. There are 7 bean types in this dataset

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