<jupyter_start><jupyter_text>Model Building<jupyter_code>from keras.layers import Dense, Dropout
from keras.models import Sequential
model = Sequential()

model.add(Dense(64, activation = 'relu', input_shape = (13,)))
model.add(Dense(64, activation = 'relu'))
model.add(Dense(1, activation = 'relu'))

model.compile( optimizer = 'rmsprop', loss = 'mse', metrics = ['mse'])

model.fit(X_train, y_train, epochs = 80)
model.evaluate(X_test,y_test)
X_test[0]
y_test[0]

for i in range(50):

  sample = X_test[i]

  sample = np.reshape(sample, (1,13))

  print("Pred : " , model.predict(sample)[0][0] * 50)
  print("Price: " , y_test[i] * 50)
  print("---------------------------")
X_train.shape<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20416666666666666, 'fraction_numerical': 0.030555555555555555, 'mean_word_length': 4.677165354330708, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '31500', 'license': 'Apache-2.0', 'n_tokens_mistral': 292, 'n_tokens_neox': 276, 'n_words': 54, 'path': 'House Price Prediction/House_Price_Prediction.ipynb', 'repo_name': 'teenage-coder/Data-Science-with-Python', 'size': '720'}