**🚀 Machine Learning Project Code 🚀 is a collection of popular Machine Learning projects which are a part of Machine Learning Course at EnjoyAlgorithms along with their Python codes.

⭐️ Major Highlights

• All Fundamentals of Machine Learning is discussed thoroughly.
• Hands-on experience with all forms of Data: Structured and Unstructured.
• All popular ML algorithms are discussed with mathematical details.
• 20+ industrial applications of Machine Learning.
• Guidance from Industry professionals to crack ML interviews.

💻 Make your system enabled to support all these projects with terminal commands

Step 1: Make a virtual environment via python

python3 -m venv ~/venv_enjoyalgorithm_projects

Step 2: Activate the virtual environment

source ~/venv_enjoyalgorithm_projects/bin/activate

Step 3: Upgrade pip

pip3 install --upgrade pip

Step 4: Clone the repository

git clone https://github.com/enjoyalgorithms/Machine-learning-project-code.git

Step 5: Change the directory

cd Machine-learning-project-code

Step 6: Install dependencies

pip3 install -r requirements.txt

Step 7: Open the jupyter-notebook to run these ipynb files

jupyter-notebook

👩‍💻 Machine Learning Projects and Python Codes

As a part of the ML curriculum, there are 15+ ML projects along with their Python codes. List of all the projects are:

Supervised Learning Projects	Algorithm Used	Code
Life Expectancy Predictio	Linear Regression	Code
Optical Character Recognition	Logistic Regression	Code
Breast Cancer Prediction	SVM	Code
Email Spam Classification	KNN	Code
Wine Quality Prediction	KNN	[Code]
Sentiment Analysis	Naive Bayes	Code
How Uber Uses ML?	Random Forest	Code
Pubg Cheater Detection	Random Forest	Code
Drug Discovery	XG-Boost	Code

Unsupervised Learning Projects	Algorithm Used	Code
Personality Prediction	k-means	Code
Customer Segmentation	Hierarchical Clustering	[Code]
Image Compression	PCA	Code

🤝 Contributing

We welcome community contributions to expand the scope of Industry Based ML Projects. Please follow the famous fork-and-pull Git workflow to add new projects.

1. Fork the repo on GitHub
2. Clone the project to your own machine
3. Commit changes to your own branch
4. Push your work back up to your fork
5. Submit a Pull request so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!

📃 License

MIT