mudaças pré formatação do pc

previsão de lançamento/sales_prediction_proxy.py

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+# file: sales_prediction_proxy.py
+
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.neighbors import NearestNeighbors
+from xgboost import XGBRegressor
+from scipy.sparse import hstack
+import joblib
+
+# =====================
+# Load Data
+# =====================
+data_path = r"C:\Users\joao.herculano\Documents\products.csv" # Update with your path
+df = pd.read_csv(data_path)
+
+# =====================
+# Preprocessing Pipeline
+# =====================
+categorical_features = ['brand', 'category', 'subcategory']
+numeric_features = ['price']
+text_feature = 'description'
+target = 'sales_in_release'
+
+df[text_feature] = df[text_feature].fillna('')
+
+text_vectorizer = TfidfVectorizer(max_features=100)
+categorical_transformer = Pipeline([
+    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
+    ('onehot', OneHotEncoder(handle_unknown='ignore'))
+])
+numeric_transformer = Pipeline([
+    ('imputer', SimpleImputer(strategy='mean')),
+    ('scaler', StandardScaler())
+])
+
+preprocessor = ColumnTransformer([
+    ('text', text_vectorizer, text_feature),
+    ('cat', categorical_transformer, categorical_features),
+    ('num', numeric_transformer, numeric_features)
+])
+
+# =====================
+# Fit Preprocessing
+# =====================
+X = preprocessor.fit_transform(df)
+y = df[target].values
+
+# Save preprocessors
+joblib.dump(preprocessor, 'models/preprocessor.joblib')
+
+# =====================
+# Train Sales Prediction Model
+# =====================
+xgb_model = XGBRegressor(n_estimators=100, random_state=42)
+xgb_model.fit(X, y)
+joblib.dump(xgb_model, 'models/sales_predictor.joblib')
+
+# =====================
+# Fit Proxy Finder Model
+# =====================
+knn = NearestNeighbors(n_neighbors=5, metric='cosine')
+knn.fit(X)
+joblib.dump(knn, 'models/proxy_finder.joblib')
+
+# =====================
+# Functions
+# =====================
+def predict_sales(new_product: dict) -> float:
+    preprocessor = joblib.load('models/preprocessor.joblib')
+    model = joblib.load('models/sales_predictor.joblib')
+
+    df_new = pd.DataFrame([new_product])
+    df_new['description'] = df_new['description'].fillna('')
+    X_new = preprocessor.transform(df_new)
+    prediction = model.predict(X_new)
+    return float(prediction[0])
+
+def get_proxies(new_product: dict, top_n=5) -> list:
+    preprocessor = joblib.load('models/preprocessor.joblib')
+    knn = joblib.load('models/proxy_finder.joblib')
+
+    df_new = pd.DataFrame([new_product])
+    df_new['description'] = df_new['description'].fillna('')
+    X_new = preprocessor.transform(df_new)
+
+    distances, indices = knn.kneighbors(X_new, n_neighbors=top_n)
+
+    proxies = df.iloc[indices[0]].copy()
+    proxies['distance'] = distances[0]
+    return proxies.to_dict(orient='records')
+
+# =====================
+# Example Usage
+# =====================
+if __name__ == '__main__':
+    new_product = {
+        'description': 'Wireless Noise Cancelling Headphones',
+        'brand': 'SoundCore',
+        'category': 'Electronics',
+        'subcategory': 'Headphones',
+        'price': 99.99
+    }
+
+    sales_estimate = predict_sales(new_product)
+    print(f"Predicted Sales: {sales_estimate}")
+
+    proxies = get_proxies(new_product, top_n=5)
+    for proxy in proxies:
+        print(f"Proxy: {proxy['description']} | Brand: {proxy['brand']} | Sold: {proxy['sales_in_release']} | Distance: {proxy['distance']:.4f}")