# TensorFlow Predictive Analysis Program

This program utilizes TensorFlow to perform predictive analysis on a dataset. It is designed to generate predictions
based on a given dataset and evaluate the results.

## Overview

The program follows these main steps:

1. **Load the dataset**
2. **Preprocess the data**
3. **Define the model architecture**
4. **Compile and train the model**
5. **Generate predictions**
6. **Evaluate the predictions**

## Installation

To run this program, you'll need Python installed on your system along with the following libraries:

- numpy
- pandas
- scikit-learn
- TensorFlow

You can install these libraries using pip:

```bash
pip install numpy pandas scikit-learn tensorflow

Usage

1. Clone the repository:

git clone https://github.com/Bobpick/Machine-Learning-Lottery


cd Machine-Learning-Lottery

2. Run the program:

python Treasure_TF.py

Program Details

The program performs the following tasks:

1. Data Loading: Loads the dataset from a CSV file.
2. Data Preprocessing: Preprocesses the data by scaling and splitting it into training and test sets.
3. Model Definition: Defines a neural network model using TensorFlow's Keras API.
4. Model Training: Compiles and trains the model using the training data.
5. Prediction Generation: Generates predictions using the trained model.
6. Evaluation: Evaluates the predictions by calculating the sum of each line and the arithmetic complexity (AC) of the predicted numbers.

License

This project is licensed under the MIT License - see the LICENSE file for details.