• This graphical user interface (GUI) is designed to facilitate the exploration and application of supervised learning techniques for regression and classification problems. It provides an interactive environment to perform preprocessing steps, visualize data, and apply various regression and classification models.

1. Data Preprocessing

• Loading Data: Upload your dataset in common formats (CSV, Excel, etc.). View basic statistics and information about the dataset.
• Handling Missing Values: Impute or remove missing values using various strategies.
• Feature Scaling: Standardize or normalize features to ensure consistent scales.
• Encoding Categorical Variables: Encode categorical variables using techniques like one-hot encoding.

2. Data Visualization

• Exploratory Data Analysis (EDA): Generate visualizations to understand the distribution of features and target variables.
• Correlation Analysis: Explore the correlation between different features and the target variable.

3. Regression Models

• Linear Regression
• Decision Trees for Regression
• Support Vector Regression
• Random Forest Regression

4. Classification Models

• Logistic Regression
• Decision Trees for Classification
• Random Forest
• Support Vector Classification
• K-NN

Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.

• This GUI was developed using Tkinter for the graphical interface.
• Machine learning models were implemented using scikit-learn.
• Visualization tools leveraged Matplotlib and Seaborn.
• Feel free to customize this README.md according to your specific implementation details and preferences.