https://github.com/saba-gul/exploratory-data-analysis-and-statistical-analysis-notebooks
This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets.
https://github.com/saba-gul/exploratory-data-analysis-and-statistical-analysis-notebooks
colab-notebook exploratory-data-analysis jupyter-notebooks machine-learning-algorithms outlier-detection outlier-removal regression-analysis statistical-analysis statistics-for-data-science
Last synced: 3 months ago
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This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets.
- Host: GitHub
- URL: https://github.com/saba-gul/exploratory-data-analysis-and-statistical-analysis-notebooks
- Owner: Saba-Gul
- Created: 2024-05-07T06:39:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-13T11:23:30.000Z (over 1 year ago)
- Last Synced: 2025-03-03T05:26:42.221Z (7 months ago)
- Topics: colab-notebook, exploratory-data-analysis, jupyter-notebooks, machine-learning-algorithms, outlier-detection, outlier-removal, regression-analysis, statistical-analysis, statistics-for-data-science
- Language: Jupyter Notebook
- Homepage:
- Size: 6.79 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Exploratory Data Analysis and Statistical Analysis Notebooks
This repository contains a collection of Jupyter Notebooks for conducting Exploratory Data Analysis (EDA) and Statistical Analysis on various datasets. Each notebook is created using Google Colab, making it easy to run and analyze the code interactively.
## Notebooks
1. **Data Loading and Initial Exploration**
- Description: Demonstrates how to load a dataset into Colab and perform initial exploratory analysis, such as examining data types, missing values, and basic statistics.
2. **Univariate Analysis**
- Description: The notebooks focuses on univariate analysis techniques, including summary statistics, distribution plots, and outlier detection.
3. **Notebook 3: Bivariate Analysis**
- Description: Explores relationships between two variables using techniques such as scatter plots, correlation analysis, and hypothesis testing.
4. **Multivariate Analysis**
- Description: Multivariate analysis techniques, including dimensionality reduction, cluster analysis, and advanced visualization methods.
5. **Statistical Modeling**
- Description: Covers statistical modeling techniques, such as regression analysis, classification models, and time series analysis.
## Usage
To use these notebooks, simply click on the provided links to open them in Google Colab. You can then run the cells in each notebook interactively to analyze your own datasets or explore the provided examples.
## Requirements
- Google Colab account
- Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn are commonly used in these notebooks. Make sure to install any additional libraries mentioned in the notebooks.
## License
These notebooks are provided under the [MIT License](LICENSE).
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