malharb

prediction = model.predict_classes(X_test)
prediction = prediction.reshape(5370,)

data = {'True':y_test,'Predicted':prediction}
df2 = pd.DataFrame(data)

from sklearn.metrics import classification_report,confusion_matrix
print(classification_report(df2['True'],df2['Predicted']))

print(confusion_matrix(df2['True'],df2['Predicted']))

malharb

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout
from tensorflow.keras.callbacks import EarlyStopping

model = Sequential()

model.add(Dense(10,input_dim=8,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(15,activation='relu'))
model.add(Dropout(0.5))

malharb

from sklearn.model_selection import train_test_split
X = df.drop('target_class',axis=1)
y = df['target_class']

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.fit(X_train)

malharb

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('pulsar_stars.csv')
df.info()

malharb

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.show()

df = sns.load_dataset('iris')

#use machine learning for classification. Via logistic regression and KNN
#1) logistic regression