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## model.py
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
def prepare_data(df):
X = np.array(df[["Delivery_person_Age", "Delivery_person_Ratings", "distance"]])
y = np.array(df["Time_taken(min)"])
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.1, random_state=42)
return xtrain, xtest, ytrain, ytest
def train_models(xtrain, ytrain):
models = {}
# Linear Regression
lin_model = LinearRegression()
lin_model.fit(xtrain, ytrain)
models["LinearRegression"] = lin_model
# Random Forest Regressor
rf_model = RandomForestRegressor()
rf_model.fit(xtrain, ytrain)
models["RandomForest"] = rf_model
# Gradient Boosting Regressor
gb_model = GradientBoostingRegressor()
gb_model.fit(xtrain, ytrain)
models["GradientBoosting"] = gb_model
return models
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