https://github.com/akash1070/machine-learning-

Learning Machine Learning Through Data
https://github.com/akash1070/machine-learning-

area-under-curve bootstrap-sampling data-science ensemble-techniques grid-search-cv hierarchical-clustering k-fold-cross-validation k-means-clustering k-nearest-neighbours leave-one-out-cross-validation linear-regression logistic-regression machine-learning naive-bayes python random-search receiver-operating-characteristic support-vector-machine underfitting-overfitting

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Learning Machine Learning Through Data

• Host: GitHub
• URL: https://github.com/akash1070/machine-learning-
• Owner: Akash1070
• Created: 2022-09-01T06:03:30.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-09-07T11:33:28.000Z (over 3 years ago)
• Last Synced: 2025-01-29T11:52:23.063Z (about 1 year ago)
• Topics: area-under-curve, bootstrap-sampling, data-science, ensemble-techniques, grid-search-cv, hierarchical-clustering, k-fold-cross-validation, k-means-clustering, k-nearest-neighbours, leave-one-out-cross-validation, linear-regression, logistic-regression, machine-learning, naive-bayes, python, random-search, receiver-operating-characteristic, support-vector-machine, underfitting-overfitting
• Language: Jupyter Notebook
• Homepage:
• Size: 4.65 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Support: Support_Vector_Machine.ipynb

Awesome Lists containing this project

README

          

# **Machine Larning**

**Self Learning of Machine Learning Algorithm**

naive-bayes, 

linear-regression,

logistic-regression, 

support-vector-machine, 

random-search,

k-nearest-neighbours,

hierarchical-clustering, 

receiver-operating-characteristic, 

k-means-clustering, 

leave-one-out-cross-validation, 

area-under-curve, 

bootstrap-sampling, 

k-fold-cross-validation, 

underfitting-overfitting, 

grid-search-cv, 

ensemble-techniques

## Authors

- [@Akash Kumar Jha](https://github.com/Akash1070)

## Installation

To install the libraries used in this project. Follow the 

below steps:

```bash

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

%matplotlib inline

```

    

## Running Flask Api

To run tests, run the following command

```bash

  python app.py

```

## 🚀 About Me

Data Scientist Enthusiast | Petroleum Engineer Graduate | Solving Problems Using Data 

# Hi, I'm Akash! 👋

## 🔗 Links

[![github](https://img.shields.io/badge/github-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://github.com/Akash1070)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/akashkumar107/)

## Other Common Github Profile Sections

👩‍💻 I’m interested in Petroleum Engineering

🧠 I’m currently learning Data Scientist

👯‍♀️ I’m looking to collaborate on Ideas & Data

## 🛠 Skills

1. Data Scientist

2. Data Analyst

3. Business Analyst

4. Machine Learning