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Machine Learning Beginner Projects

Overview

This repository contains beginner-friendly Machine Learning projects. The goal is to apply basic ML concepts to solve real-world problems using supervised learning techniques like regression and classification.

Projects

1. Digit Recognition (MNIST)

  • Description: A project to classify handwritten digits using a Convolutional Neural Network (CNN).
  • Dataset: MNIST dataset.
  • Techniques Used: Neural Networks, Image Processing, TensorFlow/Keras.
  • File: Digit_recognition.ipynb

2. House Price Prediction

  • Description: Predicting house prices using Linear Regression based on features like area, number of rooms, and location.
  • Dataset: Housing dataset (e.g., Kaggle or Sklearn's built-in dataset).
  • Techniques Used: Linear Regression, Data Preprocessing, Feature Engineering.
  • File: House_Price_Prediction_using_Linear_Regression.ipynb

Requirements

To run the projects, install the following dependencies:

pip install numpy pandas scikit-learn matplotlib tensorflow keras

Usage

  1. Clone this repository:
    git clone https://github.com/shetashreya/machine-learning-Beginner-Projects.git
  2. Navigate to the project folder:
    cd machine-learning-Beginner-Projects
  3. Open the Jupyter Notebook or Google Colab:
    • Jupyter Notebook:
      jupyter notebook
    • Google Colab:
      • Upload the .ipynb file to Colab and run the cells.

Contributions

Feel free to contribute by adding more beginner-friendly ML projects. Fork the repository, make your changes, and submit a pull request.

Future Work

  • Adding more supervised learning projects.
  • Exploring unsupervised learning techniques like clustering.
  • Implementing deep learning models for complex tasks.

License

This repository is open-source and free to use.

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