Each project showcases different machine learning techniques and algorithms, providing practical insights into the field of data science.

Features

• Diverse Datasets: A variety of datasets for different machine learning tasks, including classification, regression, and forecasting.
• Jupyter Notebooks: Each notebook contains code and explanations for different machine learning techniques and models, making it easy to understand and replicate the results.
• Hands-on Projects: Real-world projects that demonstrate the application of machine learning algorithms.

Datasets Included

• Breast Cancer Data (Breast CancerData.csv)
• Weather Forecast Data (weather_forecast.csv)
• House Price Prediction Data (House Price Prediction dataset.xlsx)
• Food Franchise Dataset (Food Franchise dataset.xlsx)
• Various other datasets for machine learning tasks.

Technologies Used

• Python
• Jupyter Notebook
• Pandas
• Scikit-learn
• NumPy
• Matplotlib / Seaborn (for visualizations)

Getting Started

To run the Jupyter notebooks locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Shrinjita/MACHINE-LEARNING.git
   cd MACHINE-LEARNING

2. Install the required packages: If you don't have the required libraries installed, you can do so with:

   pip install -r requirements.txt

3. Run Jupyter Notebook: Start Jupyter Notebook:

   jupyter notebook

4. Open a notebook: Open any of the .ipynb files in your browser to explore the projects.