Objective: Describe the main goal of the project, which is to use machine learning techniques to predict a specific outcome based on the given dataset.
Context: Explain the challenges or complexities involved in the domain or field of study. Mention any common obstacles or issues that make the task difficult.
Significance: Highlight the importance of achieving accurate predictions and how it benefits further research, analysis, or practical applications in the field.
Goal: State the ultimate aim of the project, such as identifying specific phenomena, improving detection accuracy, or prioritizing certain areas for more detailed investigation.
This project consists of xxx Jupyter Notebooks that serve different purposes:
- Notebook1.ipynb:
For example: This notebook focuses on Exploratory Data Analysis (EDA) and training various models for pulsar star classification. It includes data preprocessing, feature engineering, model training, and evaluation... etc.
- Notebook2.ipynb:
...
- Notebook3.ipynb:
...
To set up the project locally, follow these steps:
- Clone the repository:
git clone https://github.com/daistmarco/PredictingPulsarStar.git
- Navigate to the project directory:
cd your-repository
- Install the required dependencies:
pip install -r requirements.txt
-
Download the modified dataset and place it in the project directory. The original dataset can be acquired from the link [xxx](link here).
-
...
Note: If any of the above files are missing, the corresponding functionality may not work as expected.
Once the setup is complete, you can use the provided functions, such as ann_prediction(csv_file), to make predictions on new data using the pre-trained models.
Descibe where you got the Data from. Did you get it from different sources? Provide Download links only if publicly available.
The dataset contains the following attributes:
- ...
- ...
- ...
- ...
The dataset contains a total of xxx examples, with xxx positive examples and xxx negative examples.
...
What models did you test against each other and why? How did you optimize them?
What metric did you use and why? How did you final model perform?
What happens when a prediction is made using the final function? What scripts are run?