so I'm making a project where basically i have to predict whether or not a house price is above or below its median price and to do that, I'm using this dataset from Kaggle( https://drive.google.com/file/d/1GfvKA0qznNVknghV4botnNxyH-KvODOC/view ). 1 means "Above Median" and 0 means "Below Median". I wrote this code to train a neural network and save it as a .h5 file:
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
import h5py
df = pd.read_csv('housepricedata.csv')
dataset = df.values
X = dataset[:,0:10]
Y = dataset[:,10]
min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)
X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
model = Sequential([
Dense(32, activation='relu', input_shape=(10,)),
Dense(32, activation='relu'),
Dense(1, activation='sigmoid'),
])
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
hist = model.fit(X_train, Y_train,
batch_size=32, epochs=100,
validation_data=(X_val, Y_val))
model.save("house_price.h5")
After running it, it successfully saves the .h5 file to my directory. What I want to do now is use my trained model to make predictions on a new .csv file and determine whether or not each of those are above or below median price. This is an image of the csv file in VSCode that i want it to make predictions on: csv file image As you can see, this file doesn't contain a 1(above median) or 0(below median) because that's what I want it to predict. This is the code I wrote to do that:
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
from keras.models import load_model
import h5py
df = pd.read_csv('data.csv')
dataset = df.values
X = dataset[:,0:10]
Y = dataset[:,10]
min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)
X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
model = load_model("house_price.h5")
y_pred = model.predict(X_test)
print(y_pred)
It's output is [[0.00101464]]
I have no clue what that is and why it's only returning one value even though the csv file has 4 rows. Does anyone know how I can fix that and be able to predict either a 1 or a 0 for each row in the csv file? Thank You!
As much I understand what you want! Let's Try ! This code work for me
import tensorflow
model = tensorflow.keras.models.load_model("house_price.h5")
y_pred=model.predict(X_test)
still you are not able to get visit following site 1: answer1 2: answer2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('C:\\Users\\acer\\Downloads\\housepricedata.csv')
dataset.head()
X=dataset.iloc[:,0:10]
y=dataset.iloc[:,10]
X.head()
from sklearn.preprocessing import StandardScaler
obj=StandardScaler()
X=obj.fit_transform(X)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split
(X,y,random_state=2020,test_size=0.25)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation =
'relu', input_dim = 10))
# classifier.add(Dropout(p = 0.1))
# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation
= 'relu'))
# classifier.add(Dropout(p = 0.1))
# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation
= 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics
= ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)
classifier.save("house_price.h5")
import tensorflow
model = tensorflow.keras.models.load_model("house_price.h5")
y_pred=model.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)
Both y_pred produce same output for me
Here one thing you not y_pred not contain 0 and 1 because you use sigmoid function which determine predication in probability so if(y_pred>0.5) it mean value is one
#True rep one
#false rep zero
#you can use replace function or map function of pandas to get convert true
into 1
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