Matplotlib_challenge

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Project Overview:

This project analyzes data from a pharmaceutical study conducted by Pymaceuticals, Inc., which specializes in anti-cancer treatments. The study evaluates the effectiveness of multiple drug regimens in treating squamous cell carcinoma (SCC) in mice.

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📂 Files Included

1️⃣ Data Files (Located in data/ Folder)

• Mouse_metadata.csv → Contains individual-level data (e.g., Mouse ID, Drug Regime, and Sex).

• Study_results.csv → Contains population-level data (e.g., Timepoint, Tumor Volume (mm³), and Metastic Sites).

2️⃣ Jupyter Notebook File

• Matplotlib_challenge.ipynb → Contains code executed for data processing, analysis, and visualization.

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📂 Repository Structure

Matplotlib_challenge
|── Pymaceuticals/
|  |── data/
|  |  |── Mouse_metadata.csv
|  |  |── Study_results.csv
|  |── Matplotlib_challenge.ipynb
|── .gitignore
|── README.md

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🎯 Objective:

• Clean and preprocess the dataset.

• Perform exploratory data analysis and statistical summary.

• Visualize tumor volume distribution across different drug regimens.

• Identify outliers in the tumor volume dataset.

• Investigate the correlation between mouse weight and tumor volume.

• Perform a linear regression analysis to assess the relationship between mouse weight and tumor volume in the Capomulin regimen.

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1️⃣ Dataset Information

Key Variables:

• Mouse ID: Unique identifier for each mouse.

• Timepoint: Days since the start of treatment.

• Tumor Volume (mm³): Tumor size at each timepoint.

• Drug Regimen: Treatment administered to the mouse.

• Weight (g): Mouse weight in grams.

2️⃣ Data Cleaning

• Merged the Mouse_metadata.csv and Study_results.csv into a single DataFrame.

• Checked for duplicate timepoints and removed duplicated mouse data (Mouse ID: g989).

• Ensured consistency in the cleaned dataset before performing analysis.

3️⃣ Exploratory Data Analysis (EDA)

Summary Statistics:

• Calculated mean, median, variance, standard deviation, and SEM for tumor volume per drug regimen.

• Displayed results in a summary statistics table.

Data Visualizations:

• Bar Charts showed the number of observations for each drug regimen.

• Pie Charts displayed the gender distribution of mice in the study.

• Box Plot of Tumor Volume compared the distribution of final tumor volumes across the four most promising drug regimens:

  Capomulin

  Ramicane

  Infubinol

  Ceftamin

• Identified potential outliers in Infubinol and Ceftamin.

4️⃣ Capomulin Regimen Analysis

• Line Plot: Tracked tumor volume changes over time for a single mouse (Mouse ID: l509) treated with Capomulin.

• Scatter Plot: Examined the relationship between mouse weight and tumor volume in the Capomulin regimen.

5️⃣ Correlation & Regression Analysis

• Pearson Correlation Coefficient (r-value) → 0.84 (strong positive correlation between mouse weight and tumor volume).

• Linear Regression Model:

  • Equation:

    Tumor Volume = (slope × Mouse Weight) + intercept
    

  • Plotted a regression line on the scatter plot to visualize the trend.

  • P-value: Displayed to assess statistical significance.

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🔍 Key Findings & Conclusion

✅ Capomulin & Ramicane were the most effective treatments, showing the smallest final tumor volumes.

✅ Infubinol & Ceftamin showed higher tumor volumes and had potential outliers, indicating lower effectiveness.

✅ Mouse weight was strongly correlated with tumor volume (r = 0.84).

✅ Linear regression confirmed a significant relationship between mouse weight and tumor volume.

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📌 Final Recommendation:

Further research should focus on Capomulin and Ramicane as potential treatments for squamous cell carcinoma (SCC).

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🧰 Required Dependencies

• Python 3+
• Jupyter Notebook
• Pandas (Data manipulations & analysis)
• Matplotlib (Data visualization)
• Scipy (Statistical analysis)

Ensure you have a Python version that is up to date and install the following in GitBash:

pip install pandas matplotlib scipy

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🏎️ How to Run the Project

1.) Clone this repository or download the files to your local machine.

2.) Ensure your Python installed (Recommended: Python 3.8+)

3.) Ensure the required libraries are installed in your operating environment.

4.) Launch Jupyter Notebook and run the file cells sequentially (Kernel → Run)

🔗 References

• Python Official Documentation: https://docs.python.org/3/

• Pandas Library: https://pandas.pydata.org/

• Matplotlib Library: https://matplotlib.org/

• SciPy Library: https://scipy.org/

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