<jupyter_start><jupyter_text>ROOT MEAN SQUARED ERROR<jupyter_code>Err = metrics.mean_squared_error(y_test, prediction)
Err
sap = np.sqrt(Err)
sap
X = web[['bedrooms','bathrooms','sqft_living','sqft_lot','floors','waterfront','view','condition','sqft_above','sqft_basement','yr_built','yr_renovated']]
y = web[['price']]
sns.boxplot(X)
plt.show()
sns.boxplot(y)
plt.show()
sns.heatmap(X.corr())
plt.show()
plt.plot(X,y,)
plt.xlabel('keys')
plt.ylabel('value')
plt.title('Corresponding Ratio')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20676691729323307, 'fraction_numerical': 0.0, 'mean_word_length': 11.113636363636363, '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': '487981', 'license': 'Apache-2.0', 'n_tokens_mistral': 238, 'n_tokens_neox': 221, 'n_words': 26, 'path': 'house price prediction.ipynb', 'repo_name': 'ikeonly/iks-program-tech', 'size': '532'}