A No-Code Machine Learning Training Platform
This repository provides a user-friendly no-code machine learning training interface using Streamlit. It allows users to upload datasets, preprocess data, select models, and train them with just a few clicks. The project automates the entire ML workflow, from data handling to model evaluation, making it accessible for both beginners and professionals.
✔ Upload CSV Files or select from predefined datasets.
✔ Automatic Data Preprocessing: Handles categorical & numerical data, applies scaling, and splits into train-test sets.
✔ Multiple ML Models: Train with Logistic Regression, SVM, Random Forest, or XGBoost.
✔ Custom Scaling Options: Choose between StandardScaler and MinMaxScaler.
✔ Performance Evaluation: Displays model accuracy after training.
✔ User-Friendly UI: Powered by Streamlit for an interactive ML experience.
🔹 Python – Core programming language.
🔹 Streamlit – Interactive UI for ML automation.
🔹 Pandas – Data manipulation and preprocessing.
🔹 Scikit-learn – Machine learning models & preprocessing utilities.
🔹 XGBoost – Powerful gradient boosting algorithm.
🔹 Streamlit Option Menu – Sidebar navigation enhancement.
1️⃣ Clone the Repository
git clone https://github.com/Nour-Zayed/Automate-ML-Model-Training_Streamlit.git
cd Automate-ML-Model-Training_Streamlit
2️⃣ Install Dependencies
pip install -r requirements.txt
3️⃣ Run the Streamlit App
streamlit run app.py
1️⃣ Upload a CSV file or select an existing dataset.
2️⃣ Choose the target column for prediction.
3️⃣ Select data scaling method (StandardScaler/MinMaxScaler).
4️⃣ Pick an ML model (Logistic Regression, SVC, Random Forest, XGBoost).
5️⃣ Train the model and get real-time accuracy results!
