Evaluating Clinical Trial Outcomes for Auraline

Rinse Project Image

In this project, I conducted a comprehensive data analysis of Auralin, an oral insulin being researched as a substitute for Novodra, a popular injectable insulin. The analysis aimed to determine whether Auralin is as effective as Novodra in managing diabetes.

The Core Challenges


  • Data Cleaning:

    Removing duplicates, handling missing values, and correcting errors to ensure the data is ready for analysis.

  • Feature Engineering::

    Creating meaningful features from raw data to improve model performance and insights.

  • Exploratory Data Analysis (EDA):

    Understanding the underlying patterns and distributions within the data to guide further analysis.

Solution


To address the core challenges in data analysis, I implemented a structured approach to ensure data accuracy and meaningful insights. By systematically cleaning and preprocessing the data, creating valuable features, and visualizing the results, I was able to overcome the complexities involved.

  • Data Assessment:

    I manually and automatically checked the data to identify and separate issues into dirty and messy categories.

  • Data Cleaning & Feature Engineering:

    Conducted data cleaning and preprocessing, and created new features to improve analysis.

  • Visualization:

    Used tools like Matplotlib and Plotly to create graphs and visualizations, helping to uncover insights.

Using a Variety of Data Sources to Gain Better Understanding


This data integration process consolidates various CSV files, streamlining analysis and visualization. By merging data from multiple CSV sources, I provided a unified view of the dataset, enhancing the client's ability to derive insights and make informed decisions. The data sources utilized for this

  • CSV

Data Source Image

Steps Followed By Me


Import Data

Import Data

Import data from four CSV files: patients, treatments, treatment_cut, and adverse_reaction.

Summarize Data

Summarize Data

Provide a summary of what the data is about.

Column Description

Column Description

Describe the columns for different tables.

Assess Data

Assess Data

Manual and automatic assessment of data to identify issues.

Clean Data

Clean Data

Resolve issues and prepare data for analysis.

Visualize Data

Visualize Data

Create different charts and graphs to gain insights.

Write Insights & Conclusion

Write Insights & Conclusion

Document insights and conclusions from the analysis.