smrup/Food-Delivery-Time-Prediction-using-Machine-Learning

1## model.py2import numpy as np3from sklearn.model_selection import train_test_split4from sklearn.linear_model import LinearRegression5from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor6 7def prepare_data(df):8    X = np.array(df[["Delivery_person_Age", "Delivery_person_Ratings", "distance"]])9    y = np.array(df["Time_taken(min)"])10    xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.1, random_state=42)11    return xtrain, xtest, ytrain, ytest12 13def train_models(xtrain, ytrain):14    models = {}15 16    # Linear Regression17    lin_model = LinearRegression()18    lin_model.fit(xtrain, ytrain)19    models["LinearRegression"] = lin_model20 21    # Random Forest Regressor22    rf_model = RandomForestRegressor()23    rf_model.fit(xtrain, ytrain)24    models["RandomForest"] = rf_model25 26    # Gradient Boosting Regressor27    gb_model = GradientBoostingRegressor()28    gb_model.fit(xtrain, ytrain)29    models["GradientBoosting"] = gb_model30 31    return models32 33