Dry Beans Classification V2 | Kaggle Competition
Unranked Competition on Kaggle held by ITI AI-Pro.
Introduction
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.
Data Fields
The dataset consists of features describing the shape of the bean and you're required to predict it's type.
- 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.
Libraries Used
- SciKitLearn:
- Models:
SVC
,TSNE
- Metrics:
classification_report
,accuracy_score
,average_precision_score
,f1_score
- Preprocessing:
LabelEncoder
,RobustScaler
- Model Selection:
train_test_split
- Models:
- NumPy
- Pandas
- SciPy
- Visualization:
matplotlib.pyplot
seaborn
plotly
Dataset
- Observations:
- Training: 10834
- Test: 2709
- Features: 16
Exploratory Data Analysis (EDA)
- Numeric Analysis (Duplicates, Null Values, Skewness, etc.)
- Visual Analysis (Boxplots, Histograms, Heatmaps, TSNE, etc.)
Data Preprocessing
- Training \ Validation Split
- Normalization
Model Training
Model Chosen: Support Vector Classifier
Model Evaluation
Metrics Used:
- F1 Score
- Recall
- Precision