-user-behavior-predict

I recently completed a project that involved analyzing online traffic data to predict user behavior using various machine learning techniques. The dataset included features like Administrative, Informational, ProductRelated, BounceRates, and Revenue, among others. One of the key challenges I faced was dealing with outliers in the dataset. To tackle this, I employed a method to replace outliers with more reasonable values using imputation techniques. This approach not only helped in reducing their impact on the analysis but also improved the overall performance of the models. Here's a brief overview of the methods I used in this project: Data preprocessing and cleaning Handling outliers through imputation Implementing machine learning models to predict user behavior Evaluating model performance and results.