ium_464906/ium_01.ipynb at 112295464c624205c979e68d5cca40f5d278fcf8

import pandas as pd
df=pd.read_csv('OrangeQualityData.csv')

df.describe(include='all')

	Size (cm)	Weight (g)	Brix (Sweetness)	pH (Acidity)	Softness (1-5)	HarvestTime (days)	Ripeness (1-5)	Color	Variety	Blemishes (Y/N)	Quality (1-5)
count	241.000000	241.000000	241.000000	241.000000	241.000000	241.000000	241.000000	241	241	241	241.000000
unique	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5	24	12	NaN
top	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deep Orange	Cara Cara	N	NaN
freq	NaN	NaN	NaN	NaN	NaN	NaN	NaN	75	21	149	NaN
mean	7.844813	205.128631	10.907884	3.473900	3.072614	15.344398	3.599585	NaN	NaN	NaN	3.817427
std	1.086002	56.461012	2.760446	0.421007	1.323630	5.323852	1.205214	NaN	NaN	NaN	1.014410
min	6.000000	100.000000	5.500000	2.800000	1.000000	4.000000	1.000000	NaN	NaN	NaN	1.000000
25%	6.900000	155.000000	8.500000	3.200000	2.000000	11.000000	3.000000	NaN	NaN	NaN	3.000000
50%	7.800000	205.000000	11.000000	3.400000	3.000000	15.000000	4.000000	NaN	NaN	NaN	4.000000
75%	8.700000	252.000000	13.400000	3.800000	4.000000	20.000000	4.500000	NaN	NaN	NaN	4.500000
max	10.000000	300.000000	16.000000	4.400000	5.000000	25.000000	5.000000	NaN	NaN	NaN	5.000000

df["Variety"].value_counts()

Variety
Cara Cara                21
Temple                   18
Star Ruby                18
Navel                    16
Moro (Blood)             16
Tangerine                14
Clementine               14
Washington Navel         14
Satsuma Mandarin         13
Ortanique (Hybrid)       13
Minneola (Hybrid)        12
Jaffa                    11
Ambiance                 11
Valencia                 11
California Valencia       7
Honey Tangerine           7
Hamlin                    5
Midsweet (Hybrid)         5
Clementine (Seedless)     4
Murcott (Hybrid)          3
Navel (Late Season)       3
Blood Orange              2
Navel (Early Season)      2
Tangelo (Hybrid)          1
Name: count, dtype: int64

df["Variety"].value_counts().plot(kind="bar")

<Axes: xlabel='Variety'>

df[["Variety","Size (cm)"]].groupby("Variety").mean()

	Size (cm)
Variety
Ambiance	7.827273
Blood Orange	9.500000
California Valencia	7.885714
Cara Cara	8.419048
Clementine	7.578571
Clementine (Seedless)	6.500000
Hamlin	8.160000
Honey Tangerine	7.742857
Jaffa	7.090909
Midsweet (Hybrid)	8.660000
Minneola (Hybrid)	7.683333
Moro (Blood)	8.475000
Murcott (Hybrid)	7.733333
Navel	7.662500
Navel (Early Season)	7.850000
Navel (Late Season)	8.400000
Ortanique (Hybrid)	7.215385
Satsuma Mandarin	7.038462
Star Ruby	8.444444
Tangelo (Hybrid)	7.200000
Tangerine	7.607143
Temple	7.638889
Valencia	7.927273
Washington Navel	8.207143

df[["Variety","Size (cm)"]].groupby("Variety").mean().plot(kind="bar")

<Axes: xlabel='Variety'>

import seaborn as sns
sns.set_theme()
sns.relplot(data=df, x="Size (cm)", y="Weight (g)", hue="Variety")
sns.relplot(data=df, x="Size (cm)", y="Weight (g)", hue="Variety")

<seaborn.axisgrid.FacetGrid at 0x2ab15b4f150>

dfv = df[df["Variety"] !=  "Cara Cara"]
sns.relplot(data=dfv, x="Size (cm)", y="Weight (g)", hue="Variety")

<seaborn.axisgrid.FacetGrid at 0x2ab180d9610>

import sklearn
from sklearn.model_selection import train_test_split
df_train, df_test = sklearn.model_selection.train_test_split(df, test_size=50, random_state=1)
df_train["Variety"].value_counts()

Variety
Star Ruby                16
Moro (Blood)             15
Cara Cara                15
Navel                    14
Temple                   14
Clementine               11
Washington Navel         11
Tangerine                11
Ortanique (Hybrid)       10
Ambiance                  9
Satsuma Mandarin          9
Jaffa                     8
Minneola (Hybrid)         8
California Valencia       7
Valencia                  7
Honey Tangerine           6
Midsweet (Hybrid)         4
Navel (Late Season)       3
Hamlin                    3
Murcott (Hybrid)          3
Clementine (Seedless)     3
Blood Orange              2
Navel (Early Season)      1
Tangelo (Hybrid)          1
Name: count, dtype: int64

df_test["Variety"].value_counts()

Variety
Cara Cara                6
Minneola (Hybrid)        4
Satsuma Mandarin         4
Valencia                 4
Temple                   4
Tangerine                3
Ortanique (Hybrid)       3
Washington Navel         3
Clementine               3
Jaffa                    3
Ambiance                 2
Star Ruby                2
Hamlin                   2
Navel                    2
Navel (Early Season)     1
Clementine (Seedless)    1
Honey Tangerine          1
Moro (Blood)             1
Midsweet (Hybrid)        1
Name: count, dtype: int64

import sklearn
from sklearn.model_selection import train_test_split
df_train, df_test = train_test_split(df, test_size=0.1, random_state=1, stratify=df["Color"])
df_train["Color"].value_counts()

Color
Deep Orange      67
Light Orange     58
Orange-Red       49
Orange           34
Yellow-Orange     8
Name: count, dtype: int64

df_test["Color"].value_counts()

Color
Deep Orange      8
Light Orange     6
Orange-Red       6
Orange           4
Yellow-Orange    1
Name: count, dtype: int64

df = df.dropna()
df.shape

(241, 11)

df.isna().sum()

Size (cm)             0
Weight (g)            0
Brix (Sweetness)      0
pH (Acidity)          0
Softness (1-5)        0
HarvestTime (days)    0
Ripeness (1-5)        0
Color                 0
Variety               0
Blemishes (Y/N)       0
Quality (1-5)         0
dtype: int64

from sklearn.preprocessing import MinMaxScaler

numeric_columns = df.select_dtypes(include=['int', 'float']).columns
scaler = MinMaxScaler(feature_range=(0, 1))

df_scaled = df.copy()
df_scaled[numeric_columns] = scaler.fit_transform(df[numeric_columns])

df_scaled.head()

	Size (cm)	Weight (g)	Brix (Sweetness)	pH (Acidity)	Softness (1-5)	HarvestTime (days)	Ripeness (1-5)	Color	Variety	Blemishes (Y/N)	Quality (1-5)
0	0.375	0.40	0.619048	0.2500	0.250	0.285714	0.750	Orange	Valencia	N	0.750
1	0.550	0.60	0.476190	0.3750	0.500	0.476190	0.875	Deep Orange	Navel	N	0.875
2	0.200	0.25	0.809524	0.1250	0.000	0.142857	1.000	Light Orange	Cara Cara	N	1.000
3	0.750	0.75	0.285714	0.6250	0.750	0.809524	0.625	Orange-Red	Blood Orange	N	0.625
4	0.625	0.55	0.571429	0.3125	0.375	0.380952	1.000	Orange	Hamlin	Y (Minor)	0.875

df['Quality (1-5)'].value_counts().head(10)

Quality (1-5)
4.0    76
5.0    52
4.5    29
3.0    26
3.5    23
2.0    14
2.5    12
1.0     9
Name: count, dtype: int64

df['Ripeness (1-5)'].value_counts().head(10)

Ripeness (1-5)
5.0    58
4.0    52
3.0    46
2.0    27
4.5    23
1.0    17
3.5    12
2.5     6
Name: count, dtype: int64

df['HarvestTime (days)'].value_counts().head(10)

HarvestTime (days)
11    23
15    16
12    16
10    15
22    14
20    14
16    13
17    13
14    13
13    13
Name: count, dtype: int64

df['HarvestTime (days)'].value_counts()

HarvestTime (days)
11    23
15    16
12    16
10    15
22    14
20    14
16    13
17    13
14    13
13    13
18    12
23    12
21    12
19    12
25     7
24     6
5      6
9      6
7      6
8      5
6      5
4      2
Name: count, dtype: int64

564 KiB Raw Blame History

564 KiB

Raw Blame History