tfidfvectorizer code

# TF-IDF vectorizer >>> Logistic Regression

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer()
Vec = vectorizer.fit_transform(df['text_column_name_after_preprocessing'])
print(vectorizer.get_feature_names())

X = df.drop('column_name', axis = 1)
y = df["Column_name"].values

#train test split:>>>>>>>>>>>
X_train_tfidf, X_test_tfidf, y_train_tfidf, y_test_tfidf = train_test_split(X, y, test_size=0.2, random_state=2020)

model_logit_tf = LogisticRegression(class_weight="balanced",solver='saga', max_iter=100)
model_logit_tf.fit(X_train_tfidf, y_train_tfidf) # fit the model 

y_pred_tfidf = model_logit_tf.predict(X_test_tfidf) # prediction

#F1 score:>>>>>>>>>
f1score_TF = f1_score(y_test_tfidf, y_pred_tfidf, average='micro')
print(f"TF-IDF Model F1 Score for Logistic Regression: {f1score_TF * 100} %")

Rcall score:>>>>>>>>>
recall_score_TF = recall_score(y_test_tfidf, model_logit_tf.predict(X_test_tfidf), average = 'macro')
print(f"TF-IDF Model Recall Score for Logistic Regression: {recall_score_TF * 100} %")

precision score:>>>>>>>>>
precision_score_TF = precision_score(y_test_tfidf, model_logit_tf.predict(X_test_tfidf), average = 'macro')
print(f"TF-IDF Model Precision Score for Logistic Regression: {precision_score_TF * 100} %")