pip install pandas scikit-learn tensorflow numpy


These libraries include Pandas for data manipulation, Scikit-learn for machine learning algorithms, TensorFlow for deep learning models, and NumPy for numerical operations.
Usage

Follow these steps to run the project:

    Clone the repository or download all the Python scripts.
    Install the necessary Python libraries as indicated in the Installation section.
    Run main.py to execute the tool. This script will use the dataset located at the specified URL, preprocess the data, train the models, and make a sample prediction.

Components
data_preprocessing.py

This script is responsible for loading a specified dataset from a URL. It performs essential data cleaning steps, such as handling missing values, and preprocesses the data by encoding categorical variables and normalizing numerical features.
model_training.py

In this script, various machine learning models such as Logistic Regression, Decision Tree, and Random Forest are trained. It also includes the training of a simple neural network model using TensorFlow.
user_prediction.py

This script allows for making predictions with the trained models based on user inputs. The user input should be formatted according to the model's requirements, typically as a dictionary matching the dataset's feature structure.
main.py

The main script orchestrates the workflow by utilizing the functionalities provided in the other scripts. It oversees the process from data preprocessing to making predictions.
Future Developments

Suggestions for future enhancements include:

    Developing a graphical user interface or a web interface for easier interaction.
    Incorporating more sophisticated machine learning and deep learning models.
    Implementing comprehensive model evaluation and comparison metrics.
    Expanding the preprocessing capabilities to handle more diverse datasets.

Contributions

Contributions to this project are welcome. If you have suggestions for improvement or have identified bugs, please feel free to contribute.