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Wine Quality Prediction

The document discusses importing libraries for machine learning, loading wine quality data, exploring the data through visualizations and statistics, preprocessing the data through feature extraction and label binarization, training a random forest classifier model on 80% of the data and evaluating it on the 20% test data achieving 92.8% accuracy, and building a predictive system to classify new wine data.

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Alisha Anjum
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97 views6 pages

Wine Quality Prediction

The document discusses importing libraries for machine learning, loading wine quality data, exploring the data through visualizations and statistics, preprocessing the data through feature extraction and label binarization, training a random forest classifier model on 80% of the data and evaluating it on the 20% test data achieving 92.8% accuracy, and building a predictive system to classify new wine data.

Uploaded by

Alisha Anjum
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Import

Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import warnings
warnings.simplefilter('ignore')

Data Collection

# Loading the dataset to a Pandas DataFrame


wine_data = pd.read_csv(r'C:\Users\ADMIN\Desktop\Projects\Data sets\winequality-red.csv')

wine_data

fixed volatile citric residual free sulfur total sulfur


chlorides density pH sulphates alcohol quality
acidity acidity acid sugar dioxide dioxide

0 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5

1 7.8 0.880 0.00 2.6 0.098 25.0 67.0 0.99680 3.20 0.68 9.8 5

2 7.8 0.760 0.04 2.3 0.092 15.0 54.0 0.99700 3.26 0.65 9.8 5

3 11.2 0.280 0.56 1.9 0.075 17.0 60.0 0.99800 3.16 0.58 9.8 6

4 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5

... ... ... ... ... ... ... ... ... ... ... ... ...

1594 6.2 0.600 0.08 2.0 0.090 32.0 44.0 0.99490 3.45 0.58 10.5 5

1595 5.9 0.550 0.10 2.2 0.062 39.0 51.0 0.99512 3.52 0.76 11.2 6

1596 6.3 0.510 0.13 2.3 0.076 29.0 40.0 0.99574 3.42 0.75 11.0 6

1597 5.9 0.645 0.12 2.0 0.075 32.0 44.0 0.99547 3.57 0.71 10.2 5

1598 6.0 0.310 0.47 3.6 0.067 18.0 42.0 0.99549 3.39 0.66 11.0 6

1599 rows × 12 columns

wine_data.shape

(1599, 12)

wine_data.head()

fixed volatile citric residual free sulfur total sulfur


chlorides density pH sulphates alcohol quality
acidity acidity acid sugar dioxide dioxide

0 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5

1 7.8 0.88 0.00 2.6 0.098 25.0 67.0 0.9968 3.20 0.68 9.8 5

2 7.8 0.76 0.04 2.3 0.092 15.0 54.0 0.9970 3.26 0.65 9.8 5

3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.9980 3.16 0.58 9.8 6

4 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5

Checking missing Values

wine_data.isnull().sum()

fixed acidity 0
volatile acidity 0
citric acid 0
residual sugar 0
chlorides 0
free sulfur dioxide 0
total sulfur dioxide 0
density 0
pH 0
sulphates 0
alcohol 0
quality 0
dtype: int64

Statistical measures

wine_data.describe()

volatile residual free sulfur total sulfur


fixed acidity citric acid chlorides density pH sulphates alcoh
acidity sugar dioxide dioxide

count 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.000000 1599.0000

mean 8.319637 0.527821 0.270976 2.538806 0.087467 15.874922 46.467792 0.996747 3.311113 0.658149 10.4229

std 1.741096 0.179060 0.194801 1.409928 0.047065 10.460157 32.895324 0.001887 0.154386 0.169507 1.0656

min 4.600000 0.120000 0.000000 0.900000 0.012000 1.000000 6.000000 0.990070 2.740000 0.330000 8.4000

25% 7.100000 0.390000 0.090000 1.900000 0.070000 7.000000 22.000000 0.995600 3.210000 0.550000 9.5000

50% 7.900000 0.520000 0.260000 2.200000 0.079000 14.000000 38.000000 0.996750 3.310000 0.620000 10.2000

75% 9.200000 0.640000 0.420000 2.600000 0.090000 21.000000 62.000000 0.997835 3.400000 0.730000 11.1000

max 15.900000 1.580000 1.000000 15.500000 0.611000 72.000000 289.000000 1.003690 4.010000 2.000000 14.9000

sns.catplot(x='quality',data = wine_data, kind = 'count')

<seaborn.axisgrid.FacetGrid at 0x219e25c3af0>

# volatile acidity vs quality


plot = plt.figure(figsize=(5,5))
sns.barplot(x='quality',y='volatile acidity',data=wine_data)

<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>


# citric acid vs quality
plot = plt.figure(figsize=(5,5))
sns.barplot(x='quality',y='citric acid',data=wine_data)

<AxesSubplot:xlabel='quality', ylabel='citric acid'>

Correlation
1. Positive Correlation
2. Negative Correlation

correlation = wine_data.corr()

# constructing a heatmap to understand the correlation between the columns


plt.figure(figsize=(10,10))
sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Blues')

<AxesSubplot:>
Data PreProcessing

X = wine_data.drop('quality',axis=1)

print(X)

fixed acidity volatile acidity citric acid residual sugar chlorides \


0 7.4 0.700 0.00 1.9 0.076
1 7.8 0.880 0.00 2.6 0.098
2 7.8 0.760 0.04 2.3 0.092
3 11.2 0.280 0.56 1.9 0.075
4 7.4 0.700 0.00 1.9 0.076
... ... ... ... ... ...
1594 6.2 0.600 0.08 2.0 0.090
1595 5.9 0.550 0.10 2.2 0.062
1596 6.3 0.510 0.13 2.3 0.076
1597 5.9 0.645 0.12 2.0 0.075
1598 6.0 0.310 0.47 3.6 0.067

free sulfur dioxide total sulfur dioxide density pH sulphates \


0 11.0 34.0 0.99780 3.51 0.56
1 25.0 67.0 0.99680 3.20 0.68
2 15.0 54.0 0.99700 3.26 0.65
3 17.0 60.0 0.99800 3.16 0.58
4 11.0 34.0 0.99780 3.51 0.56
... ... ... ... ... ...
1594 32.0 44.0 0.99490 3.45 0.58
1595 39.0 51.0 0.99512 3.52 0.76
1596 29.0 40.0 0.99574 3.42 0.75
1597 32.0 44.0 0.99547 3.57 0.71
1598 18.0 42.0 0.99549 3.39 0.66

alcohol
0 9.4
1 9.8
2 9.8
3 9.8
4 9.4
... ...
1594 10.5
1595 11.2
1596 11.0
1597 10.2
1598 11.0

[1599 rows x 11 columns]

Label Binarization

Y = wine_data['quality'].apply(lambda y_value: 1 if y_value >= 7 else 0)

print (Y)

0 0
1 0
2 0
3 0
4 0
..
1594 0
1595 0
1596 0
1597 0
1598 0
Name: quality, Length: 1599, dtype: int64

Train and Test Split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)

print(Y.shape, Y_train.shape, Y_test.shape)

(1599,) (1279,) (320,)

Model Training:
Random Forest Classifier

model = RandomForestClassifier()

model.fit(X_train, Y_train)

RandomForestClassifier()

Model Evaluation
Accuracy score

# Accuracy on test data


X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print('Accuracy:', test_data_accuracy)

Accuracy: 0.928125

Building a Predictive System

input_data1 = (7.5,0.5,0.36,6.1,0.071,17.0,102.0,0.9978,3.35,0.8,10.5)

# Changing the input data in to a numpy array

input_data_as_numpy_array = np.asarray(input_data1)

# Reshape the data as we are predicting the label for only one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

prediction = model.predict(input_data_reshaped)
print(prediction)

if prediction[0]==1:
print('Good Quality Wine')
else:
print('Bad Quality Wine')

[0]
Bad Quality Wine

input_data = (7.3,0.65,0.0,1.2,0.065,15.0,21.0,0.9946,3.39,0.47,10.0)
# Changing the input data in to a numpy array
input_data_as_numpy_array = np.asarray(input_data)

# Reshape the data as we are predicting the label for only one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

prediction = model.predict(input_data_reshaped)
print(prediction)

if prediction[0]==1:
print('Good Quality Wine')
else:
print('Bad Quality Wine')

[1]
Good Quality Wine


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