This repository encompasses three machine learning projects: Iris Flower Classification, Unemployment Analysis with Python, and Car Price Prediction with Machine Learning.
- Iris Flower Classification
- Unemployment Analysis with Python
- Car Price Prediction with Machine Learning
Objective: Develop a machine learning model to classify iris flowers into three species—Setosa, Versicolor, and Virginica—based on their measurements.
Dataset: The Iris dataset consists of 150 samples with four features: sepal length, sepal width, petal length, and petal width.
Project Structure:
Iris_Flower_Classification/data/: Contains the dataset.notebooks/: Jupyter notebooks for data exploration and model development.models/: Saved trained models.README.md: Detailed documentation specific to this project.
Objective: Analyze unemployment rates, particularly focusing on the surge during the COVID-19 pandemic, using Python.
Dataset: The project utilizes publicly available unemployment data.
Project Structure:
Unemployment_Analysis_with_Python/data/: Contains the unemployment dataset.notebooks/: Jupyter notebooks for data analysis and visualization.reports/: Generated reports and findings.README.md: Detailed documentation specific to this project.
Objective: Build a machine learning model to predict car prices based on factors such as brand reputation, features, horsepower, and mileage.
Dataset: The project employs a dataset encompassing various car features and their corresponding prices.
Project Structure:
Car_Price_Prediction_with_ML/data/: Contains the car price dataset.notebooks/: Jupyter notebooks for data preprocessing, feature engineering, and model training.models/: Saved trained models.README.md: Detailed documentation specific to this project.
Contributions to any of these projects are welcome. Please fork the repository, create a new branch for your feature or bug fix, and submit a pull request with a detailed description of your changes.