lda.py

import numpy as np  
import pandas as pd 
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix  
from sklearn.metrics import accuracy_score
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']  
dataset = pd.read_csv('input.txt')
#train=pd.read_csv('tweet.txt')
X = dataset.iloc[:, 0:4].values  
y = dataset.iloc[:, 4].values 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 
sc = StandardScaler()  
X_train = sc.fit_transform(X_train)  
X_test = sc.transform(X_test) 
lda = LDA(n_components=1)  
X_train = lda.fit_transform(X_train, y_train)  
X_test = lda.transform(X_test) 
classifier = RandomForestClassifier(max_depth=2, random_state=0)

classifier.fit(X_train, y_train)  
y_pred = classifier.predict(X_test)  
cm = confusion_matrix(y_test, y_pred)  
print(cm)  
print('Accuracy' + str(accuracy_score(y_test, y_pred)))