BlogFeedBackProject

Analysis of BlogFeedback Data Set

Project target

Predict the number of comments in a blog after the basetime

Data Description

The data used here is BlogFeedback Data Set from UCI https://archive.ics.uci.edu/ml/datasets/BlogFeedback
The data set contatins 1 training data set and 60 test data set.

Features
Rows	60021
Columns	281

Evaluation

The metric used to evaluate the performace of this model is the log transformed mean squared error. (1 / n)∑[ln(1 + y)−ƒ̂(x)]

Working Flow

Feature Engineering

1. Feature Creation - Creating new features by combining different original features, PCA, extracting feature interactions from random forest tree model, etc.
   
2. Feature selection - Filtering features by group lasso, feature inportance of random forest model
3. Feature transformation - Transform features by scaling with normalization

Models

Model	training time	Loss
RandomForest	Moderate	0.39763
KNN	Fast	0.63239
Lasso	Fast	0.49092
Neural Network	Very Slow	0.42592
GBM	Moderate	0.37127

Evaluation

After experiments with feature engineering, we evaluated the performace of Gradient Boosting Tree model

Ensemble

We further ensemble the gradient boosting tree model by model averaging to improve the performance.

Model	Error
Single	0.37128
Ensemble	0.36985

Project Members

Peiran Cao
Zhoutao Pei
Boyang Liu