https://github.com/ajay-dhangar/machine-learning-practice

Welcome to the Machine Learning Practice
https://github.com/ajay-dhangar/machine-learning-practice

Last synced: 3 months ago
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Welcome to the Machine Learning Practice

• Host: GitHub
• URL: https://github.com/ajay-dhangar/machine-learning-practice
• Owner: ajay-dhangar
• Created: 2024-05-22T00:25:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-22T00:56:39.000Z (over 1 year ago)
• Last Synced: 2025-03-28T05:41:42.209Z (6 months ago)
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Support: support_vector_machine.py

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README

          
# Machine Learning Practice

Welcome to the **Machine Learning Practice** repository! This repository contains a collection of Python scripts and notebooks designed to help you practice various machine learning techniques and algorithms.

## Table of Contents

1. [Loading Data and Visualization](#loading-data-and-visualization)

2. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)

3. [Basic Operations with NumPy and SciPy](#basic-operations-with-numpy-and-scipy)

4. [Random Variables and Probability Distributions](#random-variables-and-probability-distributions)

5. [Linear Regression Model](#linear-regression-model)

6. [K-Nearest Neighbors (KNN) Classification](#k-nearest-neighbors-knn-classification)

7. [Naive Bayes Algorithm](#naive-bayes-algorithm)

8. [Logistic Regression](#logistic-regression)

9. [Support Vector Machines (SVM)](#support-vector-machines-svm)

## Loading Data and Visualization

1. **Loading .txt File into pandas DataFrame and Drawing Bar Plots**

    - Load a .txt file into a pandas DataFrame.

    - Draw bar plots using matplotlib.

    - Select a column from the DataFrame (subFrame).

## Exploratory Data Analysis (EDA)

1. **Reading CSV Files and Exploratory Data Analysis**

    - Read a CSV file into a pandas DataFrame.

    - Use histograms, scatter plots, and box plots for EDA.

    - Compute summary statistics.

    - Create functions to access and manipulate pandas DataFrame.

## Basic Operations with NumPy and SciPy

1. **Perform Basic Operations**

    - Perform basic operations using NumPy.

    - Utilize SciPy for advanced mathematical operations.

## Random Variables and Probability Distributions

1. **Working with Random Variables and Probability Distributions**

    - Write Python code for random variables and probability distributions.

## Linear Regression Model

1. **Predict Housing Prices Using Linear Regression**

    - Implement a linear regression model.

    - Predict housing prices using the dataset from the [Kaggle Boston Housing competition](https://www.kaggle.com/c/boston-housing).

## K-Nearest Neighbors (KNN) Classification

1. **Implement KNN Classification**

    - Write Python code to implement KNN classification.

## Naive Bayes Algorithm

1. **Implement Naive Bayes Algorithm**

    - Write Python code to implement the Naive Bayes algorithm.

## Logistic Regression

1. **Implement Logistic Regression**

    - Write Python code to implement logistic regression.

## Support Vector Machines (SVM)

1. **Implement SVM for Perceptron Learning Rule**

    - Write Python code to implement SVM.

    - Understand and apply the perceptron learning rule for supervised learning of neural networks.

## How to Use This Repository

1. Clone the repository:

    ```sh

    git clone https://github.com/Ajay-Dhangar/machine-learning-practice.git

    ```

2. Navigate to the cloned repository:

    ```sh

    cd machine-learning-practice

    ```

3. Install the required dependencies:

    ```sh

    pip install -r requirements.txt

    ```

4. Run the Python scripts or Jupyter notebooks as needed.

## Contributing

Contributions are welcome! Please create a pull request or submit an issue for any bugs or enhancements.

## License

This repository is licensed under the MIT License.