https://github.com/aninditaws/questionnaire-exploratory-data-analysis
A comprehensive EDA project for analyzing questionnaire results. Includes data cleaning, descriptive statistics, and visualizations to identify trends and patterns in survey responses.
https://github.com/aninditaws/questionnaire-exploratory-data-analysis
data-cleaning-and-preprocessing descriptive-statistics exploratory-data-analysis jupyter-notebook probability-and-statistics
Last synced: 6 months ago
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A comprehensive EDA project for analyzing questionnaire results. Includes data cleaning, descriptive statistics, and visualizations to identify trends and patterns in survey responses.
- Host: GitHub
- URL: https://github.com/aninditaws/questionnaire-exploratory-data-analysis
- Owner: aninditaws
- Created: 2025-01-24T06:10:48.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T06:18:40.000Z (8 months ago)
- Last Synced: 2025-01-24T07:20:02.684Z (8 months ago)
- Topics: data-cleaning-and-preprocessing, descriptive-statistics, exploratory-data-analysis, jupyter-notebook, probability-and-statistics
- Language: HTML
- Homepage:
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Exploratory Data Analysis on Questionnaire Results
## Project Overview
This project is the final assignment for the course **II2111-19 Probability & Statistics**. The primary objective is to perform an **Exploratory Data Analysis (EDA)** on questionnaire results. The analysis includes data preprocessing, descriptive statistics, and visualization to extract meaningful insights and identify trends within the data. The findings aim to support data-driven decision-making processes.
---
## Files in the Repository
1. **18222128_Anindita_Widya_Santoso_TugasBesarProbStat.ipynb**
- A Jupyter Notebook containing Python code for the analysis. It covers all steps from data cleaning to result visualization, with detailed comments and structured code cells for replicable analysis.
2. **18222128_Anindita_Widya_Santoso_TugasBesarProbStat.html**
- An HTML version of the notebook, designed for easy sharing and viewing without requiring a Jupyter environment.
---
## Features
- **Data Preprocessing**: Handling missing values, correcting formatting issues, and preparing raw data for analysis.
- **Descriptive Statistics**: Summarizing the data with metrics such as mean, median, mode, and frequency distributions.
- **Visualization**: Creating clear and informative visualizations, including bar charts, histograms, and scatter plots, to identify patterns and trends.
- **Insights**: Extracting and interpreting key findings to provide actionable insights.
---
## Requirements
To run the Jupyter Notebook, ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Required Libraries: `pandas`, `numpy`, `matplotlib`, `seaborn`
Install the required libraries using the following command:
```bash
pip install pandas numpy matplotlib seaborn
```
---
## Usage
1. Clone the repository to your local machine.
2. Open the Jupyter Notebook:
```bash
jupyter notebook 18222128_Anindita_Widya_Santoso_TugasBesarProbStat.ipynb
```
3. Run each code cell sequentially to replicate the analysis and view the outputs.
4. For a quick review, open the HTML file in any web browser.
---
## Author
Anindita Widya Santoso (18222128)