Dynamic Machine Learning Model Selector with Automated Visualizations 🚀📊 This project automates the process of selecting the best machine learning model for a given dataset and dynamically generates insightful visualizations for the most important features. It simplifies data analysis, model selection, and visualization in one seamless workflow.
Features ✨ Automatic Model Selection 🧠: Uses LazyPredict to identify the best machine learning model (classification or regression) based on dataset characteristics. Data Cleaning 🧹: Handles missing values, duplicate rows, and feature scaling automatically. Feature Importance Analysis 🔍: Highlights the most important features using the selected model. Dynamic Visualizations 📈: Boxplots Violin plots Scatterplots Saves Visualizations 🖼️: All graphs are saved dynamically in a specified folder for easy access. Technologies Used 💻 Python 🐍 Libraries: LazyPredict for model selection Seaborn and Matplotlib for visualizations Scikit-learn for preprocessing and model training Pandas for data manipulation Setup Instructions ⚙️
- Clone the Repository 🛠️ bash Copy code git clone https://github.com/your-username/your-repo-name.git cd your-repo-name
- Install Dependencies 📦 Ensure you have Python 3.7+ installed. Then, install the required libraries:
bash Copy code pip install -r requirements.txt 3. Run the Project 🚀 Open the project in Google Colab or your local environment. Place your dataset in the working directory (or use the default dataset provided). Run the script step-by-step: Data cleaning Model selection Visualization generation 4. Access Visualizations 📂 Generated visualizations are saved in the feature_visualizations folder. You can preview or download them for analysis.
How to Use the Code 📋 Load Your Dataset: Replace the default dataset (Iris or Diabetes) with your dataset by updating the relevant part of the script:
python Copy code df = pd.read_csv("your_dataset.csv") Run the Script: Execute the script in order. The project will:
Clean your data Select the best machine learning model Generate and save visualizations for the most important features Check Results:
View the model comparison table in the output. Open the feature_visualizations folder to analyze the graphs. Folder Structure 📂 bash Copy code 📁 your-repo-name/ ├── 📂 feature_visualizations/ # Saved graphs for important features ├── main_script.py # Main Python script ├── requirements.txt # Python dependencies └── README.md # Project description Contributing 🤝 Contributions are welcome! Feel free to submit a pull request or open an issue to suggest improvements.
Try Demo: https://colab.research.google.com/drive/1Ej8Gd9SiL4Ka-2HUdngiS-UO55oq-Er4?usp=sharing
Here is demonstration: