Why Your Data Science Projects Fail: 5 Common Pitfalls and How to Overcome Them

Data science projects often encounter hurdles, leading to failure. Common issues include undefined objectives, poor data quality, lack of model interpretability, ignored biases, and weak collaboration. Solutions involve clear goal setting, data preprocessing, emphasizing interpretability, addressing biases, and ensuring open communication. Addressing these challenges can enhance project success and business impact.