<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import matplotlib.pyplot as plt
import random
random.seed(0)
import warnings
warnings.filterwarnings('ignore')
import tensorflow
from tensorflow.keras.datasets import boston_housing
train,test=boston_housing.load_data()
(features,actual_prize),_= boston_housing.load_data(test_split=0)
features.shape
actual_prize.shape
_
model=tensorflow.keras.models.Sequential([tensorflow.keras.layers.BatchNormalization(input_shape=(13,))
,tensorflow.keras.layers.Dense(1)])
model1=tensorflow.keras.models.Sequential()
model1.add(tensorflow.keras.layers.Flatten())
model1.add(tensorflow.keras.layers.Dense(1))
model.compile(optimizer='sgd',loss='mean_absolute_error')
model.fit(features,actual_prize,epochs=100,validation_split=0.3)
test_x = np.reshape([1.2, 0, 8.14e+00, 0.0e+00, 5.3e-01, 6.14e+00, 9.170e+01, 3.97e+00, 4, 3.07e+02, 2.10e+01, 3.96e+02, 1.872e+01],(-1, 13))
model.predict(test_x)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1517948717948718, 'fraction_numerical': 0.07282051282051281, 'mean_word_length': 9.061855670103093, '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': '530929', 'license': 'MIT', 'n_tokens_mistral': 433, 'n_tokens_neox': 393, 'n_words': 48, 'path': 'Regression(Boston_Housing_dataset)_NN&DL.ipynb', 'repo_name': 'MarcelJonesRaymond/AIML-Projects', 'size': '975'}