trainer.py

import pickle

import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

# Load the training data
df = pd.read_csv("data/train.csv")

# Drop row ID as it's not useful for prediction
df.drop('row ID', axis=1, inplace=True)

# Identify categorical and numerical columns
categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
numerical_cols = df.select_dtypes(include=['float64']).columns.tolist()

# Remove target variables from feature set
categorical_cols.remove('RainToday')
target = 'RainTomorrow'

# Preprocessing for numerical data
numerical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numerical_transformer, numerical_cols),
        ('cat', categorical_transformer, categorical_cols)])

# Define the model
model = XGBClassifier(random_state=0)

# Create and evaluate the pipeline
pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                           ('model', model)
                           ])

# Split data into train and test sets
X = df.drop(target, axis=1)
y = df[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Preprocessing of training data, fit model
pipeline.fit(X_train, y_train)

# Save the pipeline
with open("model.pkl", 'wb') as file:
    pickle.dump(pipeline, file)

# Evaluate the model
score = pipeline.score(X_test, y_test)
print(score)