firt commit

src/app.py

@@ -2,3 +2,59 @@
 engine = db_connect()
 
 # your code here
+
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+
+RAW_PATH = os.path.join("data", "raw", "AB_NYC_2019.csv")
+OUT_DIR = os.path.join("data", "processed")
+
+
+def load_data(path):
+    if not os.path.exists(path):
+        raise FileNotFoundError("No existe el archivo en: " + path)
+    return pd.read_csv(path)
+
+
+def clean_data(df):
+    df_clean = df.copy()
+
+    df_clean["last_review"] = pd.to_datetime(df_clean["last_review"], errors="coerce")
+    df_clean["reviews_per_month"] = df_clean["reviews_per_month"].fillna(0)
+
+    df_clean["name"] = df_clean["name"].fillna("unknown")
+    df_clean["host_name"] = df_clean["host_name"].fillna("unknown")
+
+    df_clean = df_clean[df_clean["price"].between(1, 500)]
+
+    return df_clean
+
+
+def split_and_save(df, out_dir):
+    os.makedirs(out_dir, exist_ok=True)
+
+    train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
+
+    train_path = os.path.join(out_dir, "train.csv")
+    test_path = os.path.join(out_dir, "test.csv")
+
+    train_df.to_csv(train_path, index=False)
+    test_df.to_csv(test_path, index=False)
+
+    print("Guardado train en:", train_path)
+    print("Guardado test en:", test_path)
+    print("train shape:", train_df.shape)
+    print("test shape:", test_df.shape)
+
+
+def main():
+    df = load_data(RAW_PATH)
+    df_clean = clean_data(df)
+    split_and_save(df_clean, OUT_DIR)
+
+
+if __name__ == "__main__":
+    main()
+