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I'm Farah Ibrar, originally from the United Kingdom. I hold a BSc in Biomedical Sciences (Hons) and recently completed my MSc in Biomedical Sciences (Immunology) from the University of Westminster, London.

My academic path sparked a deep interest in data analytics, visualization, and technology, particularly through Bioinformatics and R. My Master's final project in Data Science focused on innovating database storage solutions for OMICS data solidified my passion. Since then, I have been actively enhancing my skills in data&tech. Currently, I'm advancing my skills in programming and data analysis through a Python course with CFG powered by Activision.

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This Python script automates outlier detection using the Interquartile Range (IQR) method, a robust statistical technique. By calculating the IQR and defining upper and lower bounds, it identifies significant anomalies in datasets. This streamlines data cleaning, ensuring more accurate and reliable analysis.

import numpy as np

def detect_outliers(data):
    q1, q3 = np.percentile(data, [25, 75])
    iqr = q3 - q1
    lower_bound = q1 - 1.5 * iqr
    upper_bound = q3 + 1.5 * iqr

    outliers = [x for x in data if x < lower_bound or x > upper_bound]

    return outliers

if __name__ == "__main__":
    # Example usage:
    data = [12, 18, 25, 7, 10, 15, 8, 19, 3, 100, 16]  # Example dataset with an outlier (100)
    print("Detecting Outliers...")
    outliers = detect_outliers(data)
    if outliers:
        print("Outliers detected:", outliers)
    else:
        print("No outliers detected.")

I advocate for a healthy but busy lifestyle and thrive on continuous learning and new challenges. Motivational quotes keep me focused and driven, fueling my passion for growth and inspiration.

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