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Create a Python Script to Train and Save a Text Classification Model =========================================================== This assignment requires you to train a text classification model using scikit-learn and save it as a pickle file. ### Step 1: Import Libraries Import the necessary libraries, including pandas, numpy, and scikit-learn. ```python import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression ``` ### Step 2: Load the Dataset Load the dataset into a pandas DataFrame. The dataset should contain two columns: 'text' and 'label'. ```python df = pd.read_csv('dataset.csv') ``` ### Step 3: Split the Data Split the data into training and testing sets using `train_test_split`. ```python X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42) ``` ### Step 4: Vectorize the Text Data Vectorize the text data using `TfidfVectorizer`. This will convert the text into a numerical representation that can be used by the machine learning algorithm. ```python vectorizer = TfidfVectorizer() X_train_vectorized = vectorizer.fit_transform(X_train) X_test_vectorized = vectorizer.transform(X_test) ``` ### Step 5: Train the Model Train a logistic regression model on the vectorized training data using `LogisticRegression`. ```python model = LogisticRegression(max_iter=1000) model.fit(X_train_vectorized, y_train) ``` ### Step 6: Evaluate the Model Evaluate the performance of the model on the testing data using `accuracy_score`. ```python y_pred = model.predict(X_test_vectorized) print('Accuracy:', accuracy_score(y_test, y_pred)) ``` ### Step 7: Save the Model Save the trained model to a pickle file using `joblib.dump`. ```python import joblib joblib.dump(model, 'model.pkl') ``` This script will train a text classification model and save it as a pickle file named 'model.pkl'. The model can then be loaded and used for making predictions on new data.