Alteryx Projects

This project contains several projects using Alteryx,Tabluea, and Excel to do business analysis.

Project 1.1 Use Linear Regression in Excel

Use Linear Regression to predict Diamond price with visualization in Excel, based upon previous data as predictor variables.

Project 1.2 Use Logistic Regression in Alteryx

Choose from several numerical and categorical predictor variables by testing linear relationship.
Used P-value to test statistical significance. Used scatter plots to check trends.
Create a Logistic Regression model with Alteryx and justify it with Adjusted R-Square Values.

Project 4 Classification

For this binary classification project, need to classify customers into two types based upon previous data.
Separate training set, cleaned the data(missing data imputation, outliers, low variability), check if there is highly-correlated variables; check scatterplot after imputation.
Used Logistic Regression, Decision Tree, Forest Model and Boosted Tree Model to create models and compare them to pick the best.
Use Alteryx to create models and compare models with validation data, like overall accuracy, confusion matrix, and ROC curve (receiver operating characteristic).

Project 5 A/B Test a Market Dicision

Match treatment and control units from identifying control variables and checking the correlation between control variables and performance metric.
Use Alteryx to create the model and evaluate the results by check lift and significant level of T-test.

Project 6 Forecasting Sales with Time-based Regressions

Decide holdout sample size based upon continuous time interval.
Determine Trend, Seasonal, and Error components (additive or multiplicative) using time series plots.
Tried several ETS models with Alteryx with different p,d,q settings.
Tried several ARIMA models with Alteryx with different p,d,q and P, D, Q settings by plotting ACF and PACF to show the Time Series and Seasonal Difference after 1 or 2 differencing.
Use AIC(Akaike Information Criteria) and calculated errors, like RMSE and MASE (below the generic 1.0) to compare the models and pick the better ones.
Forecast with the models. Check the forecast error measurement against the holdout sample. Use calculated error measurement, choose the best model and forecast the periods.
Graph showing 95% and 80% confidence interval.

Project 7 Combine Segmentation(Clustering), Non-binary Classification, and Time-based forecasting

Use old store data to set up segmentation and use classification methods to classify new stores.
Decide optimal number of store formats based upon K-Means Cluster measurement like AR(Adjusted Rand Index) and CH(Calinski-Harabasz index) methods.
Use Alteryx to create K-Centroid Model to cluster the stores.
Use Tableau to show the locations of clustered stores on map.
Test Classification methods, like Decistion Tree, Forest Model and Boosted Model to classify new stores.
Test and choose the best-fit time-based forecasting models using ETS and ARIMA algorithms.
Apply the best model to predict future production.
Use Tableau to demonstrate the historical data, existing stores forecasts and new stores forecasts.

About

A collection of business analysis projects, using Alteryx, Tableau, and SQL, applying several data science algorithms.