👩‍💻Successfully tackling a Kaggle data science project requires a systematic and iterative approach. By understanding the problem, exploring and preprocessing the data, choosing and training models, and iterating through these steps, you’ll be well-equipped to navigate the complexities of Kaggle competitions and showcase your data science skills. Sharing with you all the approach I used as part of my #100daysofcodechallenge from Day 1 to Day 11. I worked on a Women in Data Science Datathon 2024 which was based on predicting whether a patient will receive cancer treatment in the next 90 days. The key steps involved in approaching a Kaggle data science project are: 1️⃣Choose the right competition 2️⃣Understand the problem and evaluation metric 3️⃣Exploratory Data Analysis 4️⃣Data pre processing 5️⃣Feature Engineering 6️⃣Mode Selection 7️⃣Hyper parameter tuning 8️⃣Cross validation Swipe to find more on these topics! Happy Kaggling! 📈 #datascience #dataanalytics #womenindata ...