Crowd Funding

pptx

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University of California, Berkeley *

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MISC

Subject

Computer Science

Date

Jan 9, 2024

Type

pptx

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11

Uploaded by ConstableNeutronRam141

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CROWD FUNDING -KICK STARTER DATASET By: SWETHA LENKALA
Data set Description Field Type Description Project Id Int 64 Project ID name object Project Name main_category object Project Category, Kickstarter list out 15 categories for projects category object Project Subcategory, Kickstarter list out 52 sub-categories for projects. currency object Project currency deadline object Project deadline goal float64 Project goal in USD launched object Project lunching date Pledged float64 Pledged amount state object Project Status;five status of historical projects: failed; successful, canceled, live, suspended backers Int 64 Number of Backers country object Location of project taking off usd_pledged_real float64 Amount of pledge in USD usd_goal_real float64 Amount of Goal in USD
Data Preprocessing ◦ Removed na from the dataset ◦ Calculated duration between launch date and deadline ◦ Filtered “live “ status from the dataset from state column ◦ Subset the data to 50000 rows ◦ Converted state column with successful to 1 and other project status to failed as 0
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EDA
Logistic Regression with one-hot encoding
Logistic regression
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Naïve bayes
Random Forest
Neural Networks
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Comparison & conclusion ◦ Naïve bayes has low accuracy due its assumption of independence among features. ◦ Random Forest, being an ensemble learning method, often provides robust performance. ◦ Neural networks has highest accuracy among all models due ability to capture complex relations in data. Model Accurac y Naïve bayes 72% Logistic Regression 98.3% Random Forest 98% Neural Networks 99.4%
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