def split_test_train_data():
df_wine = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',
header=None)
df_wine.columns = [
'Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of Ash',
'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
'Proanthocyanins', 'Color intensity', 'Hue',
'OD280/OD315 of diluted wines', 'Proline'
]
X, y = df_wine.iloc[:, 1:].values, df_iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
mms = MinMaxScalar()
stdsc = StandardScalar()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mmx.transform(X_test)
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, 4].values
#Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
#Feature Scaling (Zscore, it standardizes the data) no need in
from sklearn.preprocessing import StandardScalar
sc_X = StandardScalar()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
#Applying Kernal PCA
from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)
#Fitting Logistic regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifer.fit(X_train, y_train)
#Predicting the Test set results
y_pred = classifier.predict(X_test)
import numpy as np
import matplotlib.pyplot as plt
#dataset problem- classify whether the person will purchase a product or not
#age/salary independent,purchase is the dependent variable
D = pd.read_csv("Social_Network_Ads.csv")
X = D.iloc[:, [2, 3]].values
y = D.iloc[:, 4].values #depedent variable
#maybe curved or linear line for classification
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=0)
from sklearn.preprocessing import StandardScalar
sc = StandardScalar() #feature scaling[-2,+2]
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
Classfier = SVC(kernel='linear', random_state=0)
Classfier.fit(X_train, y_train)
y_pred = Classfier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#visualizing the SVM KERNELS
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),
from sklearn.preprocessing import StandardScalar
scaler = StandardScalar()
"""
1)Building a diabetes classifier
You'll be using the Pima Indians diabetes dataset to predict whether a person has diabetes using logistic regression.
There are 8 features and one target in this dataset. The data has been split into a training and test set and pre-loaded for you as X_train, y_train, X_test, and y_test.
A StandardScaler() instance has been predefined as scaler and a LogisticRegression() one as lr
"""
# Fit the scaler on the training features and transform these in one go
X_train_std = scaler.fit_transform(X_train)
# Fit the logistic regression model on the scaled training data
lr.fit(X_train_std, y_train)
# Scale the test features
X_test_std = scaler.transform(X_test)
# Predict diabetes presence on the scaled test set
y_pred = lr.predict(X_test_std)
# Prints accuracy metrics and feature coefficients
print("{0:.1%} accuracy on test set.".format(accuracy_score(y_test, y_pred)))
print(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))
"""
79.6% accuracy on test set.
{'bmi': 0.38, 'insulin': 0.19, 'glucose': 1.23, 'diastolic': 0.03, 'family': 0.34, 'age': 0.34, 'triceps': 0.24, 'pregnant': 0.04}
# Reduced data is subjected to logistic regression for the classification
##
import numpy as np
import pandas as pd
df <- pd.read.csv('wine.csv')
X = df.iloc(:,:-1).values
y = df.iloc(:,-1).values
from sklearn.model_selection import train_test_split
X_train,x_test,y_train,y_test = train_test_split(X,y,random_state=0, test_size=0.2)
from sklearn.preprocessing import StandardScalar
sc = StandardScalar()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
# Data is now ready.
from sklearn.discreminant_analysis import LinearDiscriminimantAnalysis as ldaa
lda = ldaa(n_components = 2)
X_train = lda.fit_transform(X_train,y_train)
X_test = lda.transform(X_test)
from sklearn.linear_model import LogisticRegresion
clf = LogisticRegresion(random_state = 0)
clf.fit(X_train,y_train)
from sklearn.metrics import confusion_matrix,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Social_Network.csv')
x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.25,
random_state=0)
from sklearn.preprocessing import StandardScalar
sc_x = StandardScalar()
x_train = sc_x.fit_transform(x_train)
x_test = sc_x.transform(x_test)
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(x_train, y_train)
y_pred = classifier.predict(x_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
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,
# connect imputer to the features rows and columns
imputer.fit(x[:, 1:3])
# return the new matrix with missing data filled in
x[:, 1:3] = imputer.transform(x[:, 1:3])
# Handling Categorical Data
# Features - (country, age, salary)
# One Hot Encoding
# transforms column[0] with the encoder transformer OneHotEncoder().
# Passes through to return all columns created
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])],
remainder="passthrough")
# Run transformer on matrix
x = np.array(ct.fit_transform(x))
# Labels - (purchased)
le = LabelEncoder()
y = le.fit_transform(y)
# split training and test data
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
# Feature Scaling
sc = StandardScalar()
# transform only numerical columns
x_train[:, 3:] = sc.fit_transform(x_train[:, 3:]) # all rows and columns 3+
x_test[:, 3:] = sc.fit_transform(x_test[:, 3:]) # all rows and columns 3+
sns.distplot(train.groupby('Item_Fat_Content').size())
outlet_size = pd.pivot_table(data=train,
values='Item_Outlet_Sales',
index='Item_Fat_Content',
aggfunc=[sum, np.mean])
plt.bar(outlet_size.index, outlet_size[outlet_size.columns[1]])
corr_mat = train.corr()
sns.heatmap(corr_mat)
train.isnull().sum()
train.isnotnull().sum()
train.drop('Item_Identifier', axis=1, inplace=True)
train['Outlet_Size'].fillna(train['Outlet_Size'].mode()[0], inplace=True)
train.hist()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
labelencoder.fit_transform(train['Outlet_Size'])
onehotencoder = OneHotEncoder(categorical_features='Outlet_Size')
train = onehotencoder.fit_transform(train).toarray()
train = pd.DataFrame(train)
from sklearn.preprocessing import StandardScalar
sc = StandardScalar()
sc_x = sc.fit_transform(train)