diff --git a/get_ebay_seller_revenue.py b/get_ebay_seller_revenue.py
deleted file mode 100644
index 353f8d1..0000000
--- a/get_ebay_seller_revenue.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""Estimate the total revenue of a given Ebay seller, and identify their most
-frequently reviewed products"""
-
-import requests
-from urllib.parse import urlencode, quote
-from collections import Counter
-
-from bs4 import BeautifulSoup
-import pandas as pd
-
-# URL of Ebay's customer feedback API
-BASE_URL = "https://feedback.ebay.com/fdbk/update_feedback_profile"
-
-# Username of seller
-USERNAME = "commandantcultus"
-
-# Nested dict of parameters in API query
-PARAMS = {
-    "url": {
-        "username": USERNAME,
-        "filter": "feedback_page:RECEIVED_AS_SELLER",
-        "limit": "200",
-    },
-    "module": {"modules": "FEEDBACK_SUMMARY"},
-}
-
-
-def process_review(review):
-    """Extract relevant fields from raw JSON response for one review"""
-
-    item = review["feedbackInfo"]["item"]
-    item_text = item["itemSummary"]["textSpans"][0]["text"]
-    name, item_id = item_text.split(" (#")
-
-    return {
-        "name": name,
-        "id": int(item_id.strip(")")),
-        "price": float(item["itemPrice"]["textSpans"][0]["text"].replace("US $", "")),
-    }
-
-
-if __name__ == "__main__":
-    # Fetch data from Ebay API, convert into DataFrame
-    params_str = "&".join(f"{k}={quote(urlencode(v))}" for k, v in PARAMS.items())
-    r = requests.get(url=BASE_URL, params=params_str)
-    review_dicts = r.json()["modules"]["FEEDBACK_SUMMARY"]["feedbackView"][
-        "feedbackCards"
-    ]
-    reviews = pd.DataFrame(
-        [process_review(review_dict) for review_dict in review_dicts]
-    )
-
-    # Fetch total number of sales (should be 581 as of May 2023)
-    r = requests.get(f"https://www.ebay.com/usr/{USERNAME}")
-    soup = BeautifulSoup(r.content, features="lxml")
-    total_reviews = int(
-        soup.select("div.str-seller-card__stats-content > div[title]")[1][
-            "title"
-        ].split("  ")[0]
-    )
-
-    # Estimate seller's total revenue
-    estimated_revenue = reviews["price"].mean() * total_reviews
-    print(f"Estimated revenue of seller: ${estimated_revenue:.2f}")
-
-    # Identify 5 most frequently reviewed items
-    print("Most reviewed items:")
-    print(Counter(reviews["name"]).most_common(5))