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.
- 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
- 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
To run the projects, install the following dependencies:
pip install numpy pandas scikit-learn matplotlib tensorflow keras
- Clone this repository:
git clone https://github.com/shetashreya/machine-learning-Beginner-Projects.git
- Navigate to the project folder:
cd machine-learning-Beginner-Projects
- Open the Jupyter Notebook or Google Colab:
- Jupyter Notebook:
jupyter notebook
- Google Colab:
- Upload the
.ipynb
file to Colab and run the cells.
- Upload the
- Jupyter Notebook:
Feel free to contribute by adding more beginner-friendly ML projects. Fork the repository, make your changes, and submit a pull request.
- Adding more supervised learning projects.
- Exploring unsupervised learning techniques like clustering.
- Implementing deep learning models for complex tasks.
This repository is open-source and free to use.