https://github.com/satyamgupta53/machine-learning-notebooks

Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.
https://github.com/satyamgupta53/machine-learning-notebooks

feature-engineering feature-extraction feature-selection model-training-and-evaluation python3

Last synced: about 1 month ago
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Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.

• Host: GitHub
• URL: https://github.com/satyamgupta53/machine-learning-notebooks
• Owner: satyamgupta53
• Created: 2024-04-10T08:52:34.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-27T17:49:24.000Z (8 months ago)
• Last Synced: 2024-04-27T18:30:12.090Z (8 months ago)
• Topics: feature-engineering, feature-extraction, feature-selection, model-training-and-evaluation, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 599 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning

This repository is your one-stop shop for exploring different machine learning techniques in different scenarios. We delve into the world of machine learning to tackle the challenge of creating sustainable working high-accuracy models.

This repository provides all the tools you need to get started, including:

* Code for data preparation and model training

* Feature Selection & Engineering Techniques

* Feature Extraction & PCA techniques

* Classification, Regression & Clustering Models

Whether you're a seasoned pro or a curious beginner, feel free to dive in, experiment with the code, and contribute to the model's growth!

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install any of the required libraries in your computer system.

```bash

pip install _______

```

## Usage

```python

pip install pandas

import pandas as pd

# read a csv file

data = pd.read_csv(file_path, sep=",")

# print top 5 rows

data.head()

```

## Contributing

Pull requests are welcome. For major changes, please open an issue first

to discuss what you would like to change.