<jupyter_start><jupyter_text>Random Forest CLassifier<jupyter_code>rfc = RandomForestClassifier(n_estimators = 10)
rfc.fit(X1,Y1)
psymptoms = ['loss_of_balance','visual_disturbances','depression','chest_pain']
for i in range(len(X)):
    for z in psymptoms:
        if(z == X[i]):
            l2[i] = 1
inputtest = [l2]
predictY = rfc.predict(inputtest)
print(type(predictY[0][0]))
predicted=predictY[0]
predicted = predicted.astype(int)
predicted
Y1
h='no'
for a in range(0,len(Y1)):
    if(predicted == Y1[0]):
        h='yes'
        j=a
        break
if (h=='yes'):
    print(Y1[j])
    print(Y1[0][j])
else:
    print("Not found")
h = 'no'
for i in Y1[0]:
    print(i)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17069701280227595, 'fraction_numerical': 0.02844950213371266, 'mean_word_length': 4.214814814814815, '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': '325955', 'license': 'MIT', 'n_tokens_mistral': 304, 'n_tokens_neox': 292, 'n_words': 54, 'path': 'Prediction/Disease Prediction/.ipynb_checkpoints/Disease Prediction-checkpoint.ipynb', 'repo_name': 'bhabalomkar421/Impulse_medicalAssistant', 'size': '703'}