Kickstarter data analysis project using python and power bi

In this project, I conducted a comprehensive analysis of Kickstarter data using Python and Power BI. The dataset includes information on over 375,000 Kickstarter projects from 2009 to 2017, covering various aspects such as project name, category, launch date, deadline, goal, amount pledged, number of backers, state, and country.

Objectives:

Data Cleaning and Preparation: Using Python libraries like Pandas and NumPy, I cleaned and prepared the dataset for analysis. This involved handling missing values, normalizing data, and transforming data types.

Exploratory Data Analysis (EDA): I performed EDA to uncover patterns and insights within the data. This included visualizing trends in project success rates, funding goals, and backer engagement across different categories and time periods.

Data Visualization: Leveraging Power BI, I created interactive dashboards to present the findings. These dashboards allow users to explore the data dynamically, providing actionable insights into the factors that contribute to successful Kickstarter campaigns.

Predictive Analysis: Implemented machine learning models to predict the likelihood of project success based on various features. This helps potential project creators understand the key factors that can influence their campaign outcomes.

Tools and Technologies:

Python: For data cleaning, preparation, and analysis.

Pandas and NumPy: For data manipulation and numerical operations.

Power BI: For creating interactive and insightful visualizations.

Machine Learning: For predictive analysis and modeling.

Key Insights:

Identified the most successful project categories and the optimal funding goals.

Analyzed the impact of launch timing on project success.

Discovered trends in backer engagement and funding patterns.

This project showcases my ability to handle large datasets, perform detailed analysis, and present findings in a visually appealing and interactive manner. It demonstrates my proficiency in Python, Power BI, and machine learning, making it a valuable addition to my portfolio.