Machine Learning

Repository to study ML.

Housing Project

Regression task, from the book "Hands on Machine Learning", to predict the median house price values of thousands of districts from the state of California, USA 1990.

The dataset was acquired from the author of the book: raw.githubusercontent.com/ageron/handson-ml/master/datasets/housing/housing.tgz

The code is located on machine_learning_projects/housing_project/code/housing_data.py

Food Project

Regression task to predict the protein values of thousands of food items based on major food nutrient values: carbohydrate, energy, water, fat and nitrogen.

The datasets were acquired from the U.S Department of Agriculture: fdc.nal.usda.gov/download-datasets

The code is located on machine_learning_projects/food_project/code/food_project.py

Environment Setup

1. Dependencies

• Python
• Python Packages: matplotlib, numpy, pandas, scipy, scikit-learn, joblib

2. Clone the Repository

git clone https://github.com/weslleyskah/machine_learning_projects.git

3. Navigate to the Repository

cd machine_learning_projects

4. Create and Activate a Virtual Environment and Install Python Packages

• Windows

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
venv\Scripts\activate.bat

# Install packages
pip install matplotlib numpy pandas scipy scikit-learn joblib

• Linux

sudo apt install python3-venv
python3 -m venv venv
source venv/bin/activate

sudo apt install python3-pip
python3 -m pip install matplotlib numpy pandas scipy scikit-learn joblib

5. Run

cd food_project/code
python food_project_2.py

Notes

Run Books

1. Dependencies

• Docker
• handson-ml3

2. Setup

git clone https://github.com/ageron/handson-ml3.git
cd .../handson-ml3/docker
docker-compose build
docker images

3. Run

cd .../handson-ml3/docker
docker-compose up
## open the URL on the browser

Bibliography

• Hands on Machine Learning

Repository

datasets/               Contains source and reshaped .csv files from the datasets.
datasets_src/           Local dataset files.
food_project/           Food dataset analysis using U.S. Department of Agriculture data.
housing_project/        Housing project from "Hands-On Machine Learning" by Aurélien Géron.
img/                    Visualization images generated using Matplotlib.
models/                 Trained machine learning models.
venv/                   Python virtual environment for dependency management.
.gitignore              Files and folders to be ignored.
README.md               Notes.
handsonml_exercises/    Archive of exercises from the book "Hands on Machine Learning".