import pandas as pd import re import spacy from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.pipeline import Pipeline from sklearn.metrics import accuracy_score, classification_report from sklearn.linear_model import LogisticRegression

Load the data

data = pd.read_csv('symptomssingle.csv')

Check for any missing values and remove them

data = data.dropna()

Define a function to separate symptoms and diseases from the text

def separate_symptoms_and_diseases(text): symptoms = re.findall(r'{"symptoms":"(.?)"}', text) disease = re.sub(r'(?:{"symptoms":".?"},?)+', '', text).strip() disease = disease.replace('],', '').strip() # Remove '],' from the disease name return symptoms, disease

Apply the function to the data

data['symptoms_and_diseases'] = data['data'].apply(separate_symptoms_and_diseases) data[['symptoms', 'disease']] = pd.DataFrame(data['symptoms_and_diseases'].tolist(), index=data.index) data = data.drop(columns=['data', 'symptoms_and_diseases'])

Load the spaCy model

nlp = spacy.load('en_core_web_sm')

Preprocessing function

def preprocess(symptoms): processed_symptoms = [] for symptom in symptoms: doc = nlp(symptom) processed_symptom = ' '.join(token.lemma_.lower() for token in doc if not token.is_stop and token.is_alpha) processed_symptoms.append(processed_symptom) return ' '.join(processed_symptoms)

Preprocess the symptoms column

data['symptoms_preprocessed'] = data['symptoms'].apply(preprocess)

Split the data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(data['symptoms_preprocessed'], data['disease'], test_size=0.2, random_state=42)

Create a pipeline for text classification

pipeline = Pipeline([ ('tfidf', TfidfVectorizer(ngram_range=(1, 2))), ('classifier', LogisticRegression(solver='liblinear', C=10)) ])

Train the model

pipeline.fit(X_train, y_train)

Make predictions

y_pred = pipeline.predict(X_test)

Evaluate the model

print("Accuracy: ", accuracy_score(y_test, y_pred)) print("Classification Report:\n", classification_report(y_test, y_pred))