Cancer-Prediction-System

import pandas as pd dataset=pd.read_csv("Cancer_Data.csv") y=dataset['diagnosis'] import seaborn as sns y_cat=pd.get_dummies(y) x=dataset.drop("diagnosis",axis=1) from keras.models import Sequential model=Sequential() from keras.layers import Dense model.get_config() model.add( Dense( kernel_initializer="zero", units=4, input_shape=(32,), activation="relu", bias_initializer="zero", ) ) model.add( Dense( kernel_initializer="zero", units=3, input_shape=(32,), activation="relu", bias_initializer="zero", ) ) model.add( Dense( kernel_initializer="zero", units=2, input_shape=(32,), activation="relu", bias_initializer="zero", ) ) model.compile(loss="categorical_crossentropy") model.fit(x,y_cat) dataset=pd.read_csv("Cancer_Data.csv") y=dataset['diagnosis'] import seaborn as sns y_cat=pd.get_dummies(y) x=dataset.drop("diagnosis",axis=1) from keras.models import Sequential model=Sequential() from keras.layers import Dense model.get_config() model.add( Dense( kernel_initializer="zero", units=4, input_shape=(32,), activation="relu", bias_initializer="zero", ) ) model.add(Dense( kernel_initializer="zero", units=3, input_shape=(32,), activation="relu", bias_initializer="zero",)) model.add(Dense( kernel_initializer="zero", units=2, input_shape=(32,), activation="relu", bias_initializer="zero",)) model.compile(loss="categorical_crossentropy") model.fit(x,y_cat)