Update data/preprocess/preprocess.py

data/preprocess/preprocess.py

@@ -0,0 +1,44 @@
+import os
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
+
+price_path = os.path.join(BASE_DIR, "data", "raw", "BTC_USD_price.csv")
+output_path = os.path.join(BASE_DIR, "data", "processed", "merged_features.csv")
+os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+print(f"📂 Reading price data from: {price_path}")
+if not os.path.exists(price_path):
+    raise FileNotFoundError(f"❌ File not found: {price_path}")
+
+price_df = pd.read_csv(price_path)
+print("📊 Available columns in CSV:", list(price_df.columns))
+
+date_col = None
+for col in price_df.columns:
+    if 'date' in col.lower() or 'time' in col.lower():
+        date_col = col
+        break
+
+if date_col is None:
+    raise ValueError("❌ No date/time column found in the CSV.")
+
+price_df[date_col] = pd.to_datetime(price_df[date_col])
+price_df = price_df.set_index(date_col).sort_index()
+
+for col in price_df.columns:
+    price_df[col] = pd.to_numeric(price_df[col], errors='coerce')
+
+price_df = price_df.ffill().dropna()
+
+if price_df.empty:
+    raise ValueError("❌ DataFrame is empty after cleaning.")
+
+scaler = MinMaxScaler()
+scaled_values = scaler.fit_transform(price_df)
+normalized_df = pd.DataFrame(scaled_values, columns=price_df.columns, index=price_df.index)
+
+normalized_df.to_csv(output_path)
+print(f"✅ Preprocessed and saved to: {output_path}")