kickstarter-data-analysis

A project for analyzing the Kickstarter data available on Kaggle. In this project, we have created three distinct visualisations for analysing the Kickstarter data in vega lite. (Links available below).

Introduction

• The Kickstarter Data available on Kaggle consists of data for crowdfunding projects that are posted on the Kickstarter platform.
• The dataset has been first analysed in Python and relevant pre-processing has been done to derive certain features useful for analysis. The Jupyter notebook-Kickstarter analysis.ipynb consists of the pre-processing steps and generates CSV files for each analysis. These CSV files have been stored in another repository.
• Vega-Lite has been used to create visualisations and the JSON spec has been embedded into HTML files.
• The HTML files for the 3 analyses consist of the details of the dataset description, the visualisations and insights obtained from the analysis.

Steps to view the Analysis

Assuming you have GIT Bash already installed, or any other tool to interact with GitHub,

1. Clone the project folder

      git clone https://github.com/shwetajoshi601/kickstarter-data-analysis.git
   

2. Change directory

      cd kickstarter-data-analysis
   

3. Run the HTML

   Double click on any HTML file. It will open in the browser.
   

   Example:

   

Analysing how Project goal amount affects the success

• In this visualisation, we try to find a relation between the success/failure of a project to the goal amount the projects have.
• We find the total number of projects successful and failed in each category of Projects and compare it with the median Goal and Pledged amount for that category.

Links: HTML-Vis1.html | Processed dataset

No. of Failed and Successful Projects by Hour of Day

• The success of a project depends on multiple factors - the Goal amount of the project, project category, the title of the project or even the time at which the project was posted.
• We try to analyse if the hour of day at which the project was launched has any effect on the success.
• For this, hour has been extracted from from the launch date. For each hour, the graph displays the total number of failed and successful projects.

Links: HTML-Vis2.html | Processed dataset

Relationship between Success Rate and No. of Backers

• In this analysis, we observe the trend in the success rate of projects over the years.
• We also try to find the relationship of the success rate with the number of backers that invest in the projects.

Links: HTML-Vis3.html | Processed dataset