Kickstarter: The Business Behind Dreams

By Sam Celarek

"How might we use machine learning to determine feasible fundraising goals for a tabletop boardgame company on Kickstarter?"

🎯 Project Overview

Crafted a thorough business report using SQL, EDA, and classification models on 150,000+ entries to set viable fundraising targets for a tabletop board game campaign on Kickstarter.

📊 Dataset

The data for this project is derived from Kickstarter’s campaigns, encompassing a wide range of fields and attributes about each project.

🧹 Data Wrangling

Data from Kickstarter was imported, cleaned, and processed to ensure the integrity and consistency of the dataset.

🛠️ Feature Engineering

Based on the data’s characteristics, new features were generated to facilitate the model’s understanding and prediction of successful Kickstarter campaigns.

📶 Exploratory Data Analysis (EDA)

EDA was performed using various data visualization techniques to understand the patterns, trends, and relationships within the dataset.

📈 Analysis

Machine learning models were employed to predict the feasibility of fundraising goals, providing valuable insights for tabletop board game campaigns.

Kickstarter Image

Thank you for your interest in Kickstarter: The Business Behind Dreams. For any further inquiries or insights, please feel free to reach out through this GitHub repository or at scelarek@gmail.com.

Best Wishes,
Sam Celarek