📊 statistics_workout

This repository contains a collection of Python scripts that explore fundamental concepts in statistics using real-world datasets. These exercises cover techniques such as percentile-based filtering, Z-score calculations, modified Z-tests, and cosine similarity, enhanced with data visualization using Seaborn and Matplotlib.

📁 Contents

File Name	Description
1_percentile.py	Calculates percentiles and removes outliers based on the 90th percentile of household size.
2_mean_absolute_deviation_standard_deviation_z_value.py	Performs outlier detection using standard deviation and Z-scores on BMI data.
3_log.py	Visualizes highway population data and introduces logarithmic plotting.
4_Normal.py	Plots income vs credit limit with log-scaled axes using Seaborn.
5_cosine.py	Demonstrates cosine similarity and cosine distance for basic NLP-like document vectors.
6_modified_z_test.py	Implements both standard Z-score and Modified Z-score methods for income-based outlier detection.
modified_z_score.xlsx	Example Excel sheet supporting the modified Z-score implementation.

🔧 Technologies Used

• Python 3.x
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

📌 Key Concepts

• Descriptive Statistics
• Percentile Analysis
• Outlier Detection (Z-score, Modified Z-score)
• Data Cleaning & Preprocessing
• Cosine Similarity & Distance
• Data Visualization

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/statistics_workout.git
   cd statistics_workout

2. Install required libraries:

   pip install pandas numpy matplotlib seaborn scikit-learn

3. Run any script using:

   python filename.py

⚠️ Ensure that the necessary CSV files are placed in the correct paths or update the paths in the scripts accordingly.

📥 Download

Click here to download this repository as a ZIP file

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📫 Contact

If you have questions or suggestions, feel free to reach out via GitHub Issues.