Tahsin-Zaman-Jilan/Robi-Datathon-2.0

# Robi-Datathon-2.0 # ML Label Prediction Solution ## Overview This repository contains a solution developed to predict the labels in a dataset using machine learning techniques. The project includes preprocessing of data using MinMaxScaler and the implementation of a neural network model using the Keras API, which achieved an accuracy of 86%. ## Problem Statement The goal of this project is to predict the labels in a dataset. The dataset was preprocessed using the MinMaxScaler technique to scale the features. The model was trained using a neural network implemented with Keras. ## Pre-processing Technique The MinMaxScaler pre-processing technique was applied to normalize the numerical features in the dataset. This ensures that all features have the same scale, which is often necessary for the proper functioning of machine learning algorithms. ## Model Details The machine learning model used for this project is a neural network created with the Keras API. The architecture of the neural network includes the following layers: - Input layer with 54 nodes. - Two hidden layers with 54 nodes each and ReLU activation. - Two additional hidden layers with 25 nodes each and ReLU activation. - Output layer with 1 node and a sigmoid activation function, suitable for binary classification tasks. ## Model Performance The trained neural network model achieved an accuracy of 86% on the test dataset. The model's evaluation and classification report are available in the project. ## Project Structure The project is structured as follows: - `data/`: Contains the dataset used for training and testing. - `notebooks/`: Jupyter notebooks used for data exploration, preprocessing, and model development. - `models/`: Saved model files. - `scripts/`: Python scripts used for data preprocessing, model training, and evaluation. - `README.md`: This documentation. ## Getting Started To get started with this project, you can clone the repository and explore the notebooks, scripts, and the data …