"""The business requirement is to classify their customers of wine shop in to 3 segments so that promoting particular items 
to similar category customer would give reasonable profits. So, the independent features are 'Alcohol', 
'Malic_Acid', 'Ash', 'Ash_Alcanity', 'Magnesium', 'Total_Phenols', 'Flavanoids', 'Nonflavanoid_Phenols',
'Proanthocyanins', 'Color_Intensity', 'Hue', 'OD280', 'Proline'. Since there are many independent variables and might even have 
correlation between them, using PCA Dimensionality Reduction would be a great choice."""


##importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

##importing the dataset
dataset = pd.read_csv('Wine.csv')
X = dataset.iloc[:,0:13].values
y = dataset.iloc[:,13].values

##spliting dataset in to training and testing dataset
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 0)

##feature scaling(feature scaling is importent for pca)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

##pca modeling
"""from sklearn.decomposition import PCA
pca = PCA(n_components = None)
x_train = pca.fit_transform(x_train)
x_test = pca.transform(x_test)
explained_variance = pca.explained_variance_ratio_

##by taking n_components= None we can get the explained variance considering comulative of explained_variance
##so here the first two can expaline 56 percent of variance(36.8 + 19.3) so we take two, even beacuse we can visualize it.
##here we replace n_components = 2"""

from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_

##fitting the logistic regesssion
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

##predictions with model
y_pred = classifier.predict(X_test)

##making the confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# By using PCA dimentionality reduction technique and modeling we got the accuracy around 97.2 %.

# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
plt.title('Logistic Regression (Training set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()

# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
plt.title('Logistic Regression (Test set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()



