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<!DOCTYPE HTML>
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<title>Analysing Kickstarter Data</title>
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<header class="major">
<span class="date">February 01, 2025</span>
<h2> Analysing KickStarter Projects </h2>
<!--<p><b>Tools</b>: PostgreSQL, Juypter Notebook</p>-->
</header>
<!--<div class="image main"><img src="images/analytics 1.jfif" alt="" /></div> -->
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>1. Introduction</h3>
<p>Kickstarter is a global crowdfunding platform designed to help creators bring their projects
to life. It serves as a bridge between individuals or organizations with creative ideas and
backers who provide financial support. As data analysts for a startup considering crowdfunding
on Kickstarter, our objective is to analyze past Kickstarter projects to determine key factors
contributing to their success or failure. This analysis will assist our management team in
making data-driven decisions about which projects to launch.</p>
<h4>1.1 Research Questions</h4>
<p style="line-height: 0%;">We aim to answer the following questions:</p>
<ol>
<li>What are the success rates across different project categories?</li>
<li>Which countries have the highest success rates?</li>
<li>How does the funding goal affect a project's probability of success?</li>
<li>What is the relationship between the number of backers and a project's success?</li>
<!--<li>-</li>-->
</ol>
<h4>1.2 Tools</h4>
<p style="line-height: 0%;">The following tools were are used in this projects:</p>
<ol>
<li><b>Database Management:</b> PostgreSQL</li>
<li><b>Programming Language:</b> Python</li>
<li><b>Python Libraries:</b> Pandas, psycopg2, SQLAlchemy, Numpy, Matplotlib, Seaborn</li>
<li><b>Development Environment:</b> Jupyter Notebook</li>
<!--<li>-</li>-->
</ol>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>2. Data Colection and Preparation</h3>
<h4>2.1 Data Source</h4>
<p>The dataset was originally scraped from Kickstarter’s official website and made publicly
available on Kaggle. The dataset was downloaded in <b>CSV</b> format and stored locally for analysis.</p>
<h4>2.2 Data Cleaning Steps</h4>
<ul>
<li><b>Handling Missing Values:</b> Removed projects with missing names.
Other missing values were handled based on relevance to the analysis.</li>
<li><b>Duplicate Removal:</b> Checked and confirmed no duplicates existed in the dataset.</li>
<li><b>Column Name Standardization:</b> Renamed columns for consistency and ease of use.</li>
<li><b>Data Importation:</b> The cleaned dataset was imported into a PostgreSQL database
for structured querying and efficient analysis.</li>
</ul>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>3. Data Analysis</h3>
<h4>3.1 Success Rates by Project Category</h4>
<p>We analyzed the success rates of projects across different main categories.
<b>The top 5 categories with the highest success rates are:</b></p>
<!-- Inserting a Table-->
<table border="1">
<!--Table Caption (Title)-->
<!--<caption>Success Rate by Category</caption>-->
<!-- Table Header-->
<tr>
<th>Category</th>
<th>Success Rate</th>
</tr>
<!--Table rows-->
<tr>
<td>Dance</td>
<td>62%</td>
</tr>
<tr>
<td>Theater</td>
<td>60%</td>
</tr>
<tr>
<td>Comics</td>
<td>54%</td>
</tr>
<tr>
<td>Music</td>
<td>47%</td>
</tr>
<tr>
<td>Art</td>
<td>41%</td>
</tr>
</table>
<p><b>Insight:</b>Projects in the <b>Dance</b> and <b>Theater</b> categories are more likely to be successfully funded.</p>
<h4>3.2 Success Rates by Country</h4>
<p>The top 5 countries with the highest project success rates are:</p>
<table border="1">
<!--<caption>Success Rate by Country</caption>-->
<tr>
<th>Country</th>
<th>Success Rate</th>
</tr>
<tr>
<td>United States</td>
<td>37%</td>
</tr>
<tr>
<td>Great Britain</td>
<td>36%</td>
</tr>
<tr>
<td>Hong Kong</td>
<td>35%</td>
</tr>
<tr>
<td>Denmark</td>
<td>32%</td>
</tr>
<tr>
<td>Singapore</td>
<td>32%</td>
</tr>
</table>
<p><b>Insight:</b>Projects
launched in the United States have the highest probability of success, followed by Great Britain and Hong Kong.</p>
<h4>3.3 Effects of Funding Goal on Success Probability</h4>
<p>We categorized funding goals into four groups and analyzed their impact on success rates:</p>
<table border="1">
<!--<caption>Success Rate by Goal Category</caption>-->
<tr>
<th>Goal Category</th>
<th>Success Rate</th>
</tr>
<tr>
<td>Small Goal (<= $2,000)</td>
<td>48%</td>
</tr>
<tr>
<td>Medium Goal ($2001 - $5200)</td>
<td>40%</td>
</tr>
<tr>
<td>Large Goal ($5201 - $16,000)</td>
<td>34%</td>
</tr>
<tr>
<td>Mega Goal (> $16,000)</td>
<td>20%</td>
</tr>
</table>
<p><b>Insight:</b>Projects with smaller funding goals are more likely to succeed.
The higher the funding goal, the lower the success probability.</p>
<h4>3.4 Relationship Between Number of Backers and Project Success</h4>
<p>We categorized projects based on the number of backers:</p>
<table border="1">
<!--<caption>Success Rate by Backers Category</caption>-->
<tr>
<th>Backers Category</th>
<th>Success Rate</th>
</tr>
<tr>
<td>Large (>= 56)</td>
<td>82%</td>
</tr>
<tr>
<td>Medium (12 - 55)</td>
<td>51%</td>
</tr>
<tr>
<td>Small (2-11)</td>
<td>7%</td>
</tr>
<tr>
<td>Minimal (< 2)</td>
<td>0.5%</td>
</tr>
</table>
<p><b>Insight:</b>Projects with a higher number of backers have significantly higher chances of success.</p>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>4. Key Insights and Recomendations</h3>
<ul>
<li><b>Prioritize High-Success Categories:</b> Dance, Theater, and Comics have the highest success rates,
making them ideal choices for new projects.</li>
<li><b>Launch Projects in High-Success Countries:</b> The United States and Great Britain have the best
track records for project success.</li>
<li><b>Set Realistic Funding Goals:</b> Smaller funding goals significantly improve the chances of
success. Setting an achievable target is crucial.</li>
<li><b>Engage a Large Audience:</b> The number of backers is the strongest indicator of success. Marketing
and outreach efforts should focus on maximizing the number of contributors.</li>
<!--<li>-</li>-->
</ul>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
<h3>5. Next Steps</h3>
<p style="line-height: 0%;">For deeper analysis we recommend:</p>
<ul>
<li><b>Predictive Analysis:</b> Building machine learning models to predict the probability of project success
based on input parameters.</li>
<li><b>Prescriptive Analysis:</b> Providing actionable recommendations for optimizing funding strategies.</li>
<!--<li>-</li>
<li>-</li>
<li>-</li>-->
</ul>
<p>This project serves as a foundational analysis for understanding Kickstarter success factors,
guiding strategic decision-making for our startup.</p>
<hr style="height: 2px; border-width: 0; color:rgba(190, 188, 188, 0.986); background-color: rgba(190, 188, 188, 0.986);">
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<p>For our special problem, we were interested in how these different characteristics relate
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<p style="font-weight: bold; text-align: center;">For more details, please refer to the full analysis by clicking on the following button:</p>
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