<jupyter_start><jupyter_text>For Train-Test Evaluation Score Pick<jupyter_code>colns = winn1.pop(0)

df_cheeck = pd.DataFrame(winn1, columns=colns)
df_cheeck
df_cheeck.to_excel("Optimizing neuron48 and timestep2 unsorted relup10(Test).xlsx")
df_cheeck.sort_values(['R2SCORETEST'],ascending=[False])
df_sortt = df_cheeck.sort_values(['R2SCORETEST'],ascending=[False])
df_sortt.to_excel("Optimizing neuron48 and timestep2 relup10(Test).xlsx")<jupyter_output><empty_output><jupyter_text>Dataset Evaluation<jupyter_code>clm = data.pop(0)

dat = pd.DataFrame(data, columns=clm)
dat
dat.to_excel("##Dataset neuron70 3hls and timestep2 relup10.xlsx")<jupyter_output><empty_output><jupyter_text>For Train-Predict Evaluation Score Pick<jupyter_code>cols = predd.pop(0)

df_cheeck2 = pd.DataFrame(predd, columns=cols)
df_cheeck2
df_cheeck2.to_excel("Optimizing neuron48 and timestep2 unsorted relup10(Pred).xlsx")
df_predd = df_cheeck2.sort_values(['R2SCOREPRED'],ascending=[False])
df_predd
df_predd.to_excel("Optimizing neuron48 and timestep2 relup10(Pred).xlsx")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12258064516129032, 'fraction_numerical': 0.035023041474654376, 'mean_word_length': 10.934065934065934, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '317613', 'license': 'MIT', 'n_tokens_mistral': 473, 'n_tokens_neox': 455, 'n_words': 59, 'path': 'LSTM/Parameter-implementation.ipynb', 'repo_name': 'KachukwuOkoh/ANN-for-predictions-', 'size': '1085'}