Predictive modeling applying the entire data science lifecycle on basis of a fictious scenario: We are tasked by Kickstarter to come up with a model to predict in a first step whether a campaign is likely to be successful, given certain project parameters. In a second step (out-of-scope), Kickstarter would like to be able to provide a good goal recommendation for project creators (particularly as Kickstarter can influence certain parameters such as staff picks):

• Given certain project parameters, is a campaign likely to succeed or fail (classification)?
• What would be a reasonable goal recommendation for project creators (regression, out-of-scope)?

F1-Score was selected as the target metric to optimize prediction of state (successful/failed) on.

• Based on testing with various predictive models, we can summarize that it is definitely possible to correctly predict >75% of project outcomes, over a broad range of scoring metrics
• It is, however, not a trivial task to decide on the most relevant scoring metric
• For the purpose of this exercise, and assuming that correctly predicting positive and negative outcomes are both relevant - with a slightly stronger emphasis on correctly predicting successful projects - for the final evaluation f1 has been used as the scoring metric
• Based on f1, the best model was established using Random Forest Classifier with a f1 score of 0.90

• Kickstarter_analysis.ipynb (main Jupyter Notebook)
• 2020-07-02_Interim Presentation.pdf (presentation of findings)
• figures (folder containing selected plots used in presentation)
• figures/Title.png (title image used in Jupyter Notebook)

• 1 Business Understanding and Set-up
  • 1.1 Background
  • 1.2 Key Question
  • 1.3 Feature Glossary
  • 1.4 Dataset Description
  • 1.5 Target Metric
  • 1.6 Outcome/Recommendations
  • 1.7 To-Do-List /Open Questions
  • 1.8 Dashboard
• 2 Data Mining
• 3 Data Cleaning
  • 3.1 Inspection
  • 3.2 Observations
  • 3.3 Data Handling
• 4 Data Exploration
• 5 Feature Engineering
• 6 Preprocessing (Train/Test Split and Pipeline)
  • 6.1 Train/test and preprocessing (full dataset)
  • 6.2 Train/test and preprocessing (balanced dataset)
  • 6.3 Train/test and preprocessing (reduced dataset)
• 7 Predictive Modeling: Classification ("state")
  • 7.1 LogReg (with GridSearch)
  • 7.2 Random Forest Classifier
  • 7.3 Support Vector Classifier
  • 7.4 AdaBoost (Future Work)
  • 7.5 XGBoost (Future Work)
  • 7.6 Ensemble Test Multiple Models
  • 7.7 Optimization (Future Work): RandomizedSearchCV
  • 7.8 Optimization (Future Work): Training and Predicting Pipeline
• 8 Predictive Modeling (Future Work): Regression ("usd_pledged")
  • 8.1 Train/test split and preprocessing pipeline
  • 8.2 Linear regression (OLS)
  • 8.3 Linear regression (Scikit Learn)
  • 8.4 KNeighborsRegression
  • 8.5 RANSACRegressor
• 9 Data Visualization
• 10 Findings and Recommendations
  • 10.1 Raw results
  • 10.2 Findings
  • 10.3 Recommendations
• 11 Future Work
• 12 References and Useful Links/Blogs
  • 12.1 References
  • 12.2 Useful Links/Blogs

• Pandas
• NumPy
• Matplotlib
• SciPy
• Seaborn
• Math
• Datetime
• statsmodels
• Scikit Learn
• Plotly

• Fix XGBoost and AdaBoost
• Create additional meaningful and visually appealing Data Exploration plots
• Include further variables (e.g. length of title, length of description, errors in description, ...)
• Include further publicly available data (e.g. Kaggle Competition)
• Optimize Preprocessing (Train/Test Split and Pipeline --> position, redundancies, ...)
• Analyze in more detail the reasons behind cases where converted_pledged_amount < goal but state == "successful" (exchange rates?)
• Evaluate applicability of different scoring techniques (e.g. "roc_auc" yields very different results from "f1" --> which metric is more applicable depending on the business goal?)
• Streamline pipelines / reduce redundancies in code
• Build an App/Mock-up around the illustration in the pdf