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output.txt
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138 lines (128 loc) · 8.48 KB
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List of columns in teh given data are:
Index(['Temparature', 'Humidity ', 'Moisture', 'Soil Type', 'Crop Type',
'Nitrogen', 'Potassium', 'Phosphorous', 'Fertilizer Name'],
dtype='object')
List of columns in teh given data are:
Index(['temparature', 'humidity', 'moisture', 'soil_type', 'crop_type',
'nitrogen', 'potassium', 'phosphorous', 'fertilizer_name'],
dtype='object')
Shape of the data is:
(99, 9)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99 entries, 0 to 98
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 temparature 99 non-null int64
1 humidity 99 non-null int64
2 moisture 99 non-null int64
3 soil_type 99 non-null object
4 crop_type 99 non-null object
5 nitrogen 99 non-null int64
6 potassium 99 non-null int64
7 phosphorous 99 non-null int64
8 fertilizer_name 99 non-null object
dtypes: int64(6), object(3)
memory usage: 7.1+ KB
Data information is:
None
Five point summary of the data is:
count mean std min 25% 50% 75% max
temparature 99.0 30.282828 3.502304 25.0 28.0 30.0 33.0 38.0
humidity 99.0 59.151515 5.840331 50.0 54.0 60.0 64.0 72.0
moisture 99.0 43.181818 11.271568 25.0 34.0 41.0 50.5 65.0
nitrogen 99.0 18.909091 11.599693 4.0 10.0 13.0 24.0 42.0
potassium 99.0 3.383838 5.814667 0.0 0.0 0.0 7.5 19.0
phosphorous 99.0 18.606061 13.476978 0.0 9.0 19.0 30.0 42.0
Number of NA values in the data:
temparature 0
humidity 0
moisture 0
soil_type 0
crop_type 0
nitrogen 0
potassium 0
phosphorous 0
fertilizer_name 0
dtype: int64
Number of Null values in the data:
temparature 0
humidity 0
moisture 0
soil_type 0
crop_type 0
nitrogen 0
potassium 0
phosphorous 0
fertilizer_name 0
dtype: int64
Number of duplicated records are :
0
=== Initiating label encoding ===
=== Label encoding is completed ===
=== Initiating standard scaling ===
=== Standard scaling is completed ===
y_train data type: int32
y_train after conversion to category: category
Unique values in y_train: [2, 1, 6, 3, 5, 4, 0]
Categories (7, int32): [0, 1, 2, 3, 4, 5, 6]
Value counts in y_train: fertilizer_name
6 16
4 14
5 13
3 12
1 10
2 5
0 4
Name: count, dtype: int64
Shape of y_train: (74,)
Data type check for unique labels: [<class 'int'>]
y_train after conversion: 38 2
93 1
27 6
40 3
98 3
Name: fertilizer_name, dtype: int32
Predictions: [6 3 6 1 0 4 6 6 2 1 1 1 2 3 4 5 2 6 6 5 1 5 5 4 5]
Warning: Column soil_type not found in the data during inverse transformation.
Warning: Column crop_type not found in the data during inverse transformation.
=== Label decoding is completed ===
Predicted Fertilizer Names: fertilizer_name
0 Urea
1 20-20
2 Urea
3 14-35-14
4 10-26-26
5 28-28
6 Urea
7 Urea
8 17-17-17
9 14-35-14
10 14-35-14
11 14-35-14
12 17-17-17
13 20-20
14 28-28
15 DAP
16 17-17-17
17 Urea
18 Urea
19 DAP
20 14-35-14
21 DAP
22 DAP
23 28-28
24 DAP
Accuracy: 0.92
Classification Report:
precision recall f1-score support
0 1.00 0.33 0.50 3
1 0.80 1.00 0.89 4
2 0.67 1.00 0.80 2
3 1.00 1.00 1.00 2
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 5
6 1.00 1.00 1.00 6
accuracy 0.92 25
macro avg 0.92 0.90 0.88 25
weighted avg 0.94 0.92 0.91 25