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import pandas as pd
import joblib
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Function to load and prepare your dataset
def load_data(file_path):
# Load dataset
df = pd.read_csv(file_path)
df = df[['matched_skills', 'missing_skills', 'title_match_score', 'fit_score']]
return df
# Function to train the fit predictor model
def train_fit_predictor():
# Load your training data
data = load_data('./datasets/your_training_data.csv') # Adjust the path as necessary
# Prepare features and target variable
X = data[['matched_skills', 'missing_skills', 'title_match_score']]
y = data['fit_score']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize the model
model = RandomForestRegressor()
# Train the model
model.fit(X_train, y_train)
# Evaluate the model
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Model Mean Squared Error: {mse}')
# Save the model
joblib.dump(model, 'fit_score_model.joblib')
# Function to load the trained model
def load_fit_model(filename='fit_score_model.joblib'):
"""Load the trained model from a file."""
model = joblib.load(filename)
return model
def predict_fit_score(model, matched_count, missing_count, title_match):
features = np.array([[matched_count, missing_count, title_match]])
return model.predict(features)[0]
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