https://github.com/sayande01/glim_data_analytics_aimldl_statistics
In this repository i will be storing all the Jupyter notebook ipynb files and dataset files csv and excel that are used for Exploratory data analysis, statistical analysis, Machine learning etc in the Analytics class
https://github.com/sayande01/glim_data_analytics_aimldl_statistics
Last synced: about 2 months ago
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In this repository i will be storing all the Jupyter notebook ipynb files and dataset files csv and excel that are used for Exploratory data analysis, statistical analysis, Machine learning etc in the Analytics class
- Host: GitHub
- URL: https://github.com/sayande01/glim_data_analytics_aimldl_statistics
- Owner: sayande01
- Created: 2024-05-31T14:05:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-21T14:01:23.000Z (12 months ago)
- Last Synced: 2025-02-13T02:39:00.485Z (4 months ago)
- Language: Jupyter Notebook
- Size: 12.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Title
**Data Analysis and Machine Learning Repository**
## Objective
The objective of this repository is to provide a comprehensive collection of resources for performing exploratory data analysis (EDA), statistical analysis, and machine learning. This includes Jupyter notebooks with detailed explanations and code, as well as various datasets in CSV and Excel formats. The goal is to facilitate learning and practical application of data analysis and machine learning techniques.
## Description
This repository is a one-stop resource for anyone interested in data analysis and machine learning. It contains:
### Jupyter Notebooks
A diverse range of Jupyter notebooks (.ipynb) covering:
- **Exploratory Data Analysis (EDA)**: Techniques for summarizing and visualizing data to uncover patterns and insights.
- **Statistical Analysis**: Methods for applying statistical tests and models to draw meaningful conclusions from data.
- **Machine Learning**: Implementations of various algorithms for tasks such as classification, regression, and clustering.
Each notebook is designed to be self-explanatory, with clear instructions and comprehensive explanations to enhance understanding.
### Datasets
A variety of datasets provided in both CSV and Excel formats, including:
- **Clean and Processed Data**: Ready-to-use datasets for immediate analysis and modeling.
- **Raw Data**: Datasets requiring cleaning and preprocessing, ideal for practice.
- **Synthetic Data**: Artificial datasets for testing specific scenarios or hypotheses.
### Usage Instructions
1. **Clone the Repository**: Download the repository to your local machine.
```bash
git clone https://github.com/your-username/your-repository-name.git
```
2. **Explore the Notebooks**: Open and run the Jupyter notebooks to learn different techniques and methods.
3. **Leverage the Datasets**: Use the provided datasets for your own projects or follow along with the notebooks.
4. **Contribute**: Enhance the repository by adding new notebooks, datasets, or improving existing content.
### Folder Structure
- `notebooks/`: Jupyter notebooks organized by analysis type (EDA, Statistical Analysis, Machine Learning).
- `datasets/`: Datasets in CSV and Excel formats.
- `README.md`: Overview and instructions for using the repository.
### Getting Started
Navigate to the `notebooks/` directory and open any notebook with Jupyter Notebook or JupyterLab. Ensure you have the required Python packages installed, typically listed at the beginning of each notebook.
### Requirements
- Python 3.x
- Jupyter Notebook or JupyterLab
- Common libraries such as pandas, numpy, matplotlib, seaborn, scikit-learn, etc.
Install the necessary libraries with:
```bash
pip install -r requirements.txt
```
This repository is designed to support data enthusiasts, analysts, and machine learning practitioners at all levels. Whether you're starting out or looking to deepen your expertise, you'll find valuable resources to aid your learning and projects. Happy analyzing!