The purpose of this study is based on the available data, it was predicted after analyzes whether class is Seker, Barbunya, Bombay, Cali, Dermosan, Horoz or Sira.
This breast cancer database was obtained from the Faculty of Technology, Selcuk University, Turkey. Dataset is downloaded from archive.ics.uci.edu website. You can find the details of the dataset in that website. Dataset has 17 columns and 13612 rows with the header. Please include the link in your acknowledgements if you are going to share the results with database.
Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
This explanation also included in archive.ics.uci.edu website.
In this project, as stated in the title, results were obtained through Logistic Regression, Linear Discriminant Analysis and Principal Component Analysis methods.
| # | Column | Non-Null Count | Dtype |
|---|---|---|---|
| 0 | Area | 13611 non-null | int64 |
| 1 | Perimeter | 13611 non-null | float64 |
| 2 | MajorAxisLength | 13611 non-null | float64 |
| 3 | MinorAxisLength | 13611 non-null | float64 |
| 4 | AspectRation | 13611 non-null | float64 |
| 5 | Eccentricity | 13611 non-null | float64 |
| 6 | ConvexArea | 13611 non-null | int64 |
| 7 | EquivDiameter | 13611 non-null | float64 |
| 8 | Extent | 13611 non-null | float64 |
| 9 | Solidity | 13611 non-null | float64 |
| 10 | roundness | 13611 non-null | float64 |
| 11 | Compactness | 13611 non-null | float64 |
| 12 | ShapeFactor1 | 13611 non-null | float64 |
| 13 | ShapeFactor2 | 13611 non-null | float64 |
| 14 | ShapeFactor3 | 13611 non-null | float64 |
| 15 | ShapeFactor4 | 13611 non-null | float64 |
| 16 | Class | 13611 non-null | object |
Number of duplicated values: 68
After removing duplicate rows based on all columns.
Number of duplicated values: 0
Actual / Without PCA Confusion Matrix :
| 227 | 0 | 16 | 0 | 1 | 3 | 10 |
|---|---|---|---|---|---|---|
| 0 | 95 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 320 | 0 | 6 | 1 | 5 |
| 0 | 0 | 0 | 685 | 1 | 14 | 42 |
| 3 | 0 | 6 | 5 | 348 | 0 | 11 |
| 1 | 0 | 0 | 1 | 0 | 359 | 14 |
| 1 | 0 | 0 | 50 | 9 | 12 | 456 |
Accuracy score: 0.9191583610188261
Actual / With PCA Confusion Matrix :
| 169 | 0 | 64 | 0 | 2 | 2 | 20 |
|---|---|---|---|---|---|---|
| 0 | 95 | 0 | 0 | 0 | 0 | 0 |
| 62 | 0 | 266 | 0 | 4 | 1 | 6 |
| 0 | 0 | 0 | 685 | 1 | 14 | 42 |
| 0 | 0 | 13 | 4 | 346 | 0 | 10 |
| 1 | 0 | 0 | 2 | 0 | 357 | 15 |
| 0 | 0 | 0 | 53 | 14 | 13 | 448 |
Accuracy score: 0.8733850129198967
Without PCA and with PCA Confusion Matrix :
| 164 | 0 | 59 | 0 | 2 | 0 | 14 |
|---|---|---|---|---|---|---|
| 0 | 95 | 0 | 0 | 0 | 0 | 0 |
| 65 | 0 | 277 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 732 | 2 | 3 | 4 |
| 0 | 0 | 6 | 1 | 354 | 0 | 4 |
| 0 | 0 | 0 | 3 | 0 | 378 | 8 |
| 3 | 0 | 1 | 8 | 9 | 6 | 511 |
Accuracy score: 0.9269102990033222
LDA and Actual Confusion Matrix :
| 184 | 0 | 37 | 0 | 3 | 0 | 15 |
|---|---|---|---|---|---|---|
| 0 | 95 | 0 | 0 | 0 | 0 | 0 |
| 36 | 0 | 306 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 591 | 0 | 113 | 35 |
| 0 | 0 | 4 | 0 | 359 | 0 | 2 |
| 2 | 0 | 0 | 242 | 0 | 130 | 15 |
| 3 | 0 | 1 | 59 | 6 | 0 | 469 |
Accuracy score: 0.7877445551864156
Process took 3.976813316345215 seconds.
Before running the code make sure that you have these libraries:
- pandas
- time
- sklearn
- warnings
If you have something to say to me please contact me:
- Twitter: Doguilmak.
- Mail address: [email protected]
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