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import pandas as pd
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import numpy as np
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import requests
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.pipeline import Pipeline
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import joblib
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API_KEY = 'a4f54718b17aa482e0b0a9f2e6220fc0'
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WEATHER_CACHE = {}
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SEASON_MAP = {1: 'Winter', 2: 'Winter', 12: 'Winter',
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3: 'Spring', 4: 'Spring', 5: 'Spring',
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6: 'Summer', 7: 'Summer', 8: 'Summer',
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9: 'Fall', 10: 'Fall', 11: 'Fall'}
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def fetch_weather(city, state, api_key=API_KEY):
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key = f"{city},{state}"
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if key in WEATHER_CACHE:
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return WEATHER_CACHE[key]
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geo_url = f"http://api.openweathermap.org/geo/1.0/direct?q={city},{state},US&limit=1&appid={api_key}"
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try:
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geo_resp = requests.get(geo_url)
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geo_resp.raise_for_status()
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geo_data = geo_resp.json()
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if not geo_data:
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return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
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lat, lon = geo_data[0]['lat'], geo_data[0]['lon']
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weather_url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}&units=metric"
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weather_resp = requests.get(weather_url)
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weather_resp.raise_for_status()
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data = weather_resp.json()
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weather = {
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'temperature': data['main']['temp'],
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'humidity': data['main']['humidity'],
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'condition': data['weather'][0]['main']
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}
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WEATHER_CACHE[key] = weather
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return weather
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except Exception as e:
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print(f"Weather fetch error for {city}, {state}: {e}")
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return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
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def extract_season(month):
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return SEASON_MAP.get(month, 'Unknown')
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def load_and_prepare_data(csv_path):
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df = pd.read_csv(csv_path)
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df['Order Date'] = pd.to_datetime(df['Order Date'], dayfirst=True)
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df['order_month'] = df['Order Date'].dt.month
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df['order_day_of_week'] = df['Order Date'].dt.dayofweek
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df['season'] = df['order_month'].apply(extract_season)
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np.random.seed(42)
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df['discount'] = (df['Sales'] / df['Sales'].max()) * 20 + np.random.normal(0, 2, len(df))
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weather_features = df.apply(lambda row: fetch_weather(row['City'], row['State']), axis=1)
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df['temperature'] = [w['temperature'] for w in weather_features]
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df['humidity'] = [w['humidity'] for w in weather_features]
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df['condition'] = [w['condition'] for w in weather_features]
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return df
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def train_discount_model(df):
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features = [
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'Category', 'Sub-Category', 'Product ID', 'Sales',
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'City', 'State', 'Segment', 'Ship Mode',
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'order_month', 'order_day_of_week', 'season',
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'temperature', 'humidity', 'condition'
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]
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X = df[features]
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y = df['discount']
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categorical = [
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'Category', 'Sub-Category', 'Product ID', 'City', 'State',
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'Segment', 'Ship Mode', 'season', 'condition'
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]
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numeric = ['Sales', 'order_month', 'order_day_of_week', 'temperature', 'humidity']
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preprocessor = ColumnTransformer([
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('cat', OneHotEncoder(handle_unknown='ignore'), categorical),
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('num', 'passthrough', numeric)
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])
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model = Pipeline([
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('pre', preprocessor),
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('reg', RandomForestRegressor(n_estimators=100, random_state=42))
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])
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model.fit(X, y)
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return model
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if __name__ == "__main__":
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print("Loading and preparing data...")
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df = load_and_prepare_data('train.csv')
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print("Training model...")
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model = train_discount_model(df)
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joblib.dump(model, 'discount_model.joblib')
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print("Model trained and saved as discount_model.joblib.") |
