Upload 5 files

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 DiscountEngine/WMT_Grocery_202209.csv filter=lfs diff=lfs merge=lfs -text
+WMT_Grocery_202209.csv filter=lfs diff=lfs merge=lfs -text

WMT_Grocery_202209.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4b17fd9cdef56f81ffad1b0574686fefc93fda039962318d746e2a8c35d6bfe
+size 179757122

discount_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95279ead44e50e7dab7cefd662a7a880011b2ba4d85899ee8a941ab88e593df5
+size 89291098

predict_discount.py

@@ -0,0 +1,93 @@
+import pandas as pd
+import requests
+import joblib
+from datetime import datetime
+
+API_KEY = 'a4f54718b17aa482e0b0a9f2e6220fc0'
+WEATHER_CACHE = {}
+
+# Helper to map month to season
+SEASON_MAP = {1: 'Winter', 2: 'Winter', 12: 'Winter',
+              3: 'Spring', 4: 'Spring', 5: 'Spring',
+              6: 'Summer', 7: 'Summer', 8: 'Summer',
+              9: 'Fall', 10: 'Fall', 11: 'Fall'}
+
+def fetch_weather(city, state, api_key=API_KEY):
+    key = f"{city},{state}"
+    if key in WEATHER_CACHE:
+        return WEATHER_CACHE[key]
+    geo_url = f"http://api.openweathermap.org/geo/1.0/direct?q={city},{state},US&limit=1&appid={api_key}"
+    try:
+        geo_resp = requests.get(geo_url)
+        geo_resp.raise_for_status()
+        geo_data = geo_resp.json()
+        if not geo_data:
+            return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
+        lat, lon = geo_data[0]['lat'], geo_data[0]['lon']
+        weather_url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}&units=metric"
+        weather_resp = requests.get(weather_url)
+        weather_resp.raise_for_status()
+        data = weather_resp.json()
+        weather = {
+            'temperature': data['main']['temp'],
+            'humidity': data['main']['humidity'],
+            'condition': data['weather'][0]['main']
+        }
+        WEATHER_CACHE[key] = weather
+        return weather
+    except Exception as e:
+        print(f"Weather fetch error for {city}, {state}: {e}")
+        return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
+
+def extract_season(month):
+    return SEASON_MAP.get(month, 'Unknown')
+
+def predict_discount(product_serial, city, state, order_date, segment, ship_mode, df, model):
+    product = df[df['Product ID'] == product_serial]
+    if product.empty:
+        print(f"Product serial {product_serial} not found.")
+        return None
+    weather = fetch_weather(city, state)
+    # Parse order date
+    try:
+        order_dt = pd.to_datetime(order_date, dayfirst=True)
+    except Exception:
+        print("Invalid order date format. Use YYYY-MM-DD.")
+        return None
+    order_month = order_dt.month
+    order_day_of_week = order_dt.dayofweek
+    season = extract_season(order_month)
+    X_pred = pd.DataFrame({
+        'Category': [product.iloc[0]['Category']],
+        'Sub-Category': [product.iloc[0]['Sub-Category']],
+        'Product ID': [product_serial],
+        'Sales': [product.iloc[0]['Sales']],
+        'City': [city],
+        'State': [state],
+        'Segment': [segment],
+        'Ship Mode': [ship_mode],
+        'order_month': [order_month],
+        'order_day_of_week': [order_day_of_week],
+        'season': [season],
+        'temperature': [weather['temperature']],
+        'humidity': [weather['humidity']],
+        'condition': [weather['condition']]
+    })
+    discount = model.predict(X_pred)[0]
+    return max(0, round(discount, 2))
+
+if __name__ == "__main__":
+    # Load model and data
+    print("Loading model and data...")
+    model = joblib.load('discount_model.joblib')
+    df = pd.read_csv('train.csv')
+    # User input
+    serial = input("Enter product serial (Product ID): ")
+    city = input("Enter city: ")
+    state = input("Enter state: ")
+    order_date = input("Enter order date (YYYY-MM-DD): ")
+    segment = input("Enter customer segment (e.g., Consumer, Corporate, Home Office): ")
+    ship_mode = input("Enter ship mode (e.g., First Class, Second Class, Standard Class, Same Day): ")
+    discount = predict_discount(serial, city, state, order_date, segment, ship_mode, df, model)
+    if discount is not None:
+        print(f"Predicted discount for product {serial} in {city}, {state} on {order_date}: {discount}%") 

train_discount_model.py

@@ -0,0 +1,97 @@
+import pandas as pd
+import numpy as np
+import requests
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+import joblib
+
+API_KEY = 'a4f54718b17aa482e0b0a9f2e6220fc0'
+WEATHER_CACHE = {}
+
+# Helper to map month to season
+SEASON_MAP = {1: 'Winter', 2: 'Winter', 12: 'Winter',
+              3: 'Spring', 4: 'Spring', 5: 'Spring',
+              6: 'Summer', 7: 'Summer', 8: 'Summer',
+              9: 'Fall', 10: 'Fall', 11: 'Fall'}
+
+def fetch_weather(city, state, api_key=API_KEY):
+    key = f"{city},{state}"
+    if key in WEATHER_CACHE:
+        return WEATHER_CACHE[key]
+    geo_url = f"http://api.openweathermap.org/geo/1.0/direct?q={city},{state},US&limit=1&appid={api_key}"
+    try:
+        geo_resp = requests.get(geo_url)
+        geo_resp.raise_for_status()
+        geo_data = geo_resp.json()
+        if not geo_data:
+            return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
+        lat, lon = geo_data[0]['lat'], geo_data[0]['lon']
+        weather_url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}&units=metric"
+        weather_resp = requests.get(weather_url)
+        weather_resp.raise_for_status()
+        data = weather_resp.json()
+        weather = {
+            'temperature': data['main']['temp'],
+            'humidity': data['main']['humidity'],
+            'condition': data['weather'][0]['main']
+        }
+        WEATHER_CACHE[key] = weather
+        return weather
+    except Exception as e:
+        print(f"Weather fetch error for {city}, {state}: {e}")
+        return {'temperature': 20, 'humidity': 50, 'condition': 'Clear'}
+
+def extract_season(month):
+    return SEASON_MAP.get(month, 'Unknown')
+
+def load_and_prepare_data(csv_path):
+    df = pd.read_csv(csv_path)
+    # Parse dates
+    df['Order Date'] = pd.to_datetime(df['Order Date'], dayfirst=True)
+    df['order_month'] = df['Order Date'].dt.month
+    df['order_day_of_week'] = df['Order Date'].dt.dayofweek
+    df['season'] = df['order_month'].apply(extract_season)
+    # Simulate discount for training
+    np.random.seed(42)
+    df['discount'] = (df['Sales'] / df['Sales'].max()) * 20 + np.random.normal(0, 2, len(df))
+    # Fetch weather features
+    weather_features = df.apply(lambda row: fetch_weather(row['City'], row['State']), axis=1)
+    df['temperature'] = [w['temperature'] for w in weather_features]
+    df['humidity'] = [w['humidity'] for w in weather_features]
+    df['condition'] = [w['condition'] for w in weather_features]
+    return df
+
+def train_discount_model(df):
+    features = [
+        'Category', 'Sub-Category', 'Product ID', 'Sales',
+        'City', 'State', 'Segment', 'Ship Mode',
+        'order_month', 'order_day_of_week', 'season',
+        'temperature', 'humidity', 'condition'
+    ]
+    X = df[features]
+    y = df['discount']
+    categorical = [
+        'Category', 'Sub-Category', 'Product ID', 'City', 'State',
+        'Segment', 'Ship Mode', 'season', 'condition'
+    ]
+    numeric = ['Sales', 'order_month', 'order_day_of_week', 'temperature', 'humidity']
+    preprocessor = ColumnTransformer([
+        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical),
+        ('num', 'passthrough', numeric)
+    ])
+    model = Pipeline([
+        ('pre', preprocessor),
+        ('reg', RandomForestRegressor(n_estimators=100, random_state=42))
+    ])
+    model.fit(X, y)
+    return model
+
+if __name__ == "__main__":
+    print("Loading and preparing data...")
+    df = load_and_prepare_data('train.csv')
+    print("Training model...")
+    model = train_discount_model(df)
+    joblib.dump(model, 'discount_model.joblib')
+    print("Model trained and saved as discount_model.joblib.")