Machine Learning Analysis with CRISP-DM

Overview

This project applies the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology to analyze the Auto.csv dataset. It includes data preprocessing, exploratory data analysis, and implementation of both supervised and unsupervised learning models.

Features

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA) with Boxplots and other visualizations
• Feature Scaling using StandardScaler
• Machine Learning Models:
  • Supervised Learning: Regression models (e.g., Linear Regression)
  • Unsupervised Learning: Clustering (e.g., K-Means)
• Model Evaluation & Interpretation

Installation

Prerequisites

Ensure you have Python 3.8+ and the following libraries installed:

pip install numpy pandas matplotlib seaborn scikit-learn streamlit

Usage

1. Clone the repository:

   git clone https://github.com/AmineHamdi-hub/MachineLearningProject.git
   cd your-repo

2. Run code:

   streamlit run main.py

File Structure

├── data
│   ├── Auto.csv            # Dataset
├── notebooks
│   ├── ML_Amine_Hamdi.ipynb           # Exploratory Data Analysis
├── main.py
├── README.md

Authors

• Amine

License

This project is licensed under the MIT License.