Ibn-mohey / Dry-Beans-Classification-Competition

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.

Dry-Beans-Classification-Competition

This is For InClass Prediction Competition

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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

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File descriptions

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

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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.

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Technologies and algorithms used

algorithms

• Random Forest (Deprecated)
• CatBoostClassifier (Used)

technologies

• k_folds
• Bagging

Feature Engineering and Preprocessing

• A) qcut Discretize variable into equal-sized buckets based on rank or based on sample quantiles.

• B) LabelEncoder Encode target labels with value between 0 and n_classes-1 the previous Discretized values

  • Compactness
  • Extent
  • Area

• C) pandas.cut Used cut when you need to segment and sort data values into bins

  • MinorAxisLength
  • ShapeFactor1
  • Solidity
  • roundness
  • Compactness
  • ShapeFactor2
  • ShapeFactor3
  • ShapeFactor4

Training The Model

Trained using Kaggle CPU

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final Score

• CatBoost f1_score Train: 0.9408
• CatBoost f1_score test: 0.9405

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Final result

the first 2 was cheaters that used the real data for submission