Post𝐁𝐞𝐟𝐨𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥, 𝐜𝐡𝐞𝐜𝐤 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚.

𝐁𝐞𝐟𝐨𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥, 𝐜𝐡𝐞𝐜𝐤 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚. When a model performs badly, the first thing we often do is try a different algorithm. Sometimes that works. But before doing that, I usually look at the dataset. 🔍 I check things like: 🔹 Missing values 🔹 Duplicate records 🔹 Outliers 🔹 Wrong data types 🔹 Class imbalance 🔹 Data leakage 🔹 High-cardinality columns 🔹 Features with little useful information There is no point spending hours tuning a model if the dataset itself has problems. ⚠️ A simple workflow I prefer is: 📥 𝑹𝒂𝒘 𝑫𝒂𝒕𝒂 ↓ 🔎 𝑪𝒉𝒆𝒄𝒌 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 ↓ 🧹 𝑪𝒍𝒆𝒂𝒏 ↓ 📊 𝑨𝒏𝒂𝒍𝒚𝒛𝒆 ↓ 🤖 𝑻𝒓𝒂𝒊𝒏 ↓ 📈 𝑴𝒐𝒏𝒊𝒕𝒐𝒓 Data quality is not just something to deal with before machine learning. It affects every step that comes after it. So when a model is not performing as expected, don't immediately change the model. 🔍 Take another look at the data first. https://youtube.com/playlist?list=PL0nX4ZoMtjYHTtowSzzB2gVH2AuuoF9WW&si=EhLKvJCVlYQXknOs Use Data quality checker tool: https://datasetdoctor.fastapicloud.dev