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modelfinal.py
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modelfinal.py
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import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import nltk
from nltk.corpus import cmudict
import joblib
nltk.download('cmudict')
d = cmudict.dict()
# Helper function to count syllables
def syllable_count(word):
if isinstance(word, str):
try:
return max([len([y for y in x if y[-1].isdigit()]) for x in d[word.lower()]])
except KeyError:
return len([char for char in word if char in 'aeiou'])
else:
return 0
train_data = pd.read_csv("modeltraining/train_data.csv")
test_data = pd.read_csv("modeltraining/test_data.csv")
# Ensure 'word' column is treated as strings and handle NaN values
train_data['word'] = train_data['Word'].astype(str)
test_data['word'] = test_data['Word'].astype(str)
# Feature extraction
def extract_features(df):
"""
Extract features from the given DataFrame.
Parameters:
df (pd.DataFrame): Input DataFrame containing the 'Word' column.
Returns:
pd.DataFrame: DataFrame with extracted features, including word length and syllable count.
"""
# TODO: Implement the function to fill the word_length and syllable_count features.
pass
train_data = extract_features(train_data)
test_data = extract_features(test_data)
clf = RandomForestClassifier(random_state=42)
X_train = train_data[['word_length', 'syllable_count', 'Frequency']]
y_train = train_data['diff_level'].fillna(0).astype(int)
clf.fit(X_train, y_train)
y_test = test_data['diff_level'].fillna(0).astype(int)
X_test = test_data[['word_length', 'syllable_count', 'Frequency']]
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy on Test Data: {accuracy * 100:.2f}%")
#You can unedit this line if you want to save the model
#joblib.dump(clf, 'word_difficulty_model.pkl')
print("Model training complete and saved!")