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width_cm
float64
length_cm
float64
blemish_count
int64
color
string
firmness
int64
sample_id
string
2.3
2.5
4
red
1
SYN_0000
2.3
2.5
4
red
1
SYN_0001
2.1
2.1
2
red
2
SYN_0002
1.7
1.6
1
red
1
SYN_0003
2
2.3
5
red
1
SYN_0004
2
2.3
5
red
1
SYN_0005
2.3
2.5
4
red
1
SYN_0006
2.1
2.2
2
red
2
SYN_0007
2.2
2.4
0
green
3
SYN_0008
2
2.5
3
green
2
SYN_0009
2
2.5
2
green
3
SYN_0010
1.7
2.5
3
red
1
SYN_0011
2.4
2.8
1
red
3
SYN_0012
2.2
2.4
0
green
3
SYN_0013
2.3
2.6
3
red
1
SYN_0014
2.4
2.4
4
green
1
SYN_0015
2.3
2.5
2
green
3
SYN_0016
2.2
2.3
0
red
2
SYN_0017
2.3
2.6
3
red
1
SYN_0018
2.2
2.7
0
red
2
SYN_0019
2.4
2.7
4
red
1
SYN_0020
2.3
2.7
3
red
2
SYN_0021
2.2
2.4
0
red
2
SYN_0022
2.2
2.5
0
green
3
SYN_0023
2.4
2.8
1
red
3
SYN_0024
2.5
2.7
5
green
2
SYN_0025
2.2
2.6
4
red
3
SYN_0026
2.2
2.4
5
green
3
SYN_0027
2.3
2.7
3
red
2
SYN_0028
2.3
2.5
4
red
1
SYN_0029
2.2
2.4
0
red
2
SYN_0030
2.5
2.7
5
green
2
SYN_0031
2
2.5
4
green
2
SYN_0032
2.2
2.7
1
green
2
SYN_0033
2.3
2.4
4
red
1
SYN_0034
2.4
2.8
1
red
3
SYN_0035
2.2
2.4
0
green
3
SYN_0036
2.2
2.7
1
green
2
SYN_0037
2
2.5
4
green
2
SYN_0038
2
2.3
5
red
1
SYN_0039
2.4
2.7
4
red
1
SYN_0040
2.3
2.6
4
green
2
SYN_0041
2.2
2.4
0
red
2
SYN_0042
2.2
2.4
0
green
3
SYN_0043
2.2
2.4
0
red
2
SYN_0044
2.4
2.4
1
green
3
SYN_0045
1.9
2.1
2
red
1
SYN_0046
2.2
2.7
1
green
2
SYN_0047
2.3
2.4
2
green
3
SYN_0048
2.3
2.6
6
green
2
SYN_0049
2.5
2.8
3
green
3
SYN_0050
2.4
2.7
4
red
1
SYN_0051
2.1
2.2
2
red
2
SYN_0052
2.3
2.7
2
green
2
SYN_0053
2.3
2.6
6
green
2
SYN_0054
2.2
2.4
5
green
3
SYN_0055
2.5
2.8
3
green
3
SYN_0056
2.2
2.4
0
red
2
SYN_0057
2.3
2.6
6
green
2
SYN_0058
1.7
2.5
3
red
1
SYN_0059
2.1
2.1
2
red
2
SYN_0060
2.3
2.6
6
green
2
SYN_0061
2.2
2.4
5
green
3
SYN_0062
2.2
2.4
2
red
1
SYN_0063
2.3
2.4
4
red
1
SYN_0064
2.2
2.5
2
red
3
SYN_0065
2.2
2.7
2
green
3
SYN_0066
2.3
2.5
4
red
1
SYN_0067
2.2
2.4
0
green
3
SYN_0068
2.2
2.3
0
red
2
SYN_0069
2.2
2.4
0
red
2
SYN_0070
2.2
2.4
5
green
3
SYN_0071
2.1
2.4
3
green
3
SYN_0072
2.2
2.4
0
red
2
SYN_0073
2.5
2.9
1
red
3
SYN_0074
2.3
2.4
2
green
3
SYN_0075
2.5
2.8
4
red
1
SYN_0076
2.5
2.7
5
green
2
SYN_0077
2.2
2.3
0
red
2
SYN_0078
2.3
2.7
3
red
2
SYN_0079
2.3
2.6
4
green
2
SYN_0080
2.3
2.5
4
red
1
SYN_0081
2.4
2.4
4
green
1
SYN_0082
2.5
2.7
5
green
2
SYN_0083
2.1
2.1
2
red
2
SYN_0084
2.3
2.6
3
red
1
SYN_0085
2
2.5
4
green
2
SYN_0086
2.1
2.4
3
green
3
SYN_0087
2.2
2.7
2
green
3
SYN_0088
2
2.3
5
red
1
SYN_0089
2.2
2.7
1
green
2
SYN_0090
2.3
2.7
2
green
2
SYN_0091
2
2.5
3
green
2
SYN_0092
2
2.5
3
green
2
SYN_0093
2
2.5
4
green
2
SYN_0094
2
2.3
5
red
1
SYN_0095
2.3
2.6
4
green
2
SYN_0096
2.5
2.8
4
red
1
SYN_0097
2
2.5
4
green
2
SYN_0098
2.3
2.6
3
red
1
SYN_0099
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Dataset Card for Grape Firmness Dataset

Dataset Metadata

  • Purpose: The dataset is intended for training a machine learning model to predict grape firmness based on physical characteristics and blemishes.
  • Composition: The dataset contains measurements of grape width, length, blemish count, color, and firmness.
  • Collection Method: Data was collected manually through physical measurements and visual inspection of grape samples.
  • Labels: The target variable "firmness" is a categorical label with three levels: 1 (Soft), 2 (Medium), and 3 (Firm).
  • Splits:
    • original: Contains the initial 30 rows of collected data.
    • augmented: Contains 300 augmented rows generated using SMOTE-NC and numeric jitter to address class imbalance and increase dataset size.

Exploratory Data Analysis (EDA) - Original Data

Here are some visualizations showing the distribution of firmness and the relationship between physical characteristics and firmness in the original dataset.

Firmness Distribution in Original Data

Width vs Firmness in Original Data

Length vs Firmness in Original Data

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