https://github.com/uznetdev/competition-for-nt-4th-month
https://github.com/uznetdev/competition-for-nt-4th-month
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/uznetdev/competition-for-nt-4th-month
- Owner: UznetDev
- License: mit
- Created: 2024-10-11T13:49:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-01T11:57:35.000Z (over 1 year ago)
- Last Synced: 2025-03-04T14:32:52.114Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 1.64 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Competition for NT - 4th Month
This repository contains the notebook and resources for the **4th Month Competition for NT**. This competition involves analyzing a dataset, performing feature engineering, visualizations, and training a predictive model using various Python libraries.
## Table of Contents
- [Project Overview](#project-overview)
- [Dataset](#dataset)
- [Requirements](#requirements)
- [Installation](#installation)
- [Notebook Structure](#notebook-structure)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Project Overview
The main objective of this competition is to analyze the given data, explore relationships between features, and develop a model to make predictions based on the processed dataset. The notebook explores feature correlations, visualizes data distributions, and builds a machine learning model to meet the competition's objectives.
## Dataset
Details about the dataset and its contents:
- **Data Loading**: The dataset is loaded and initially explored to understand the features and target variable.
- **Preprocessing**: Includes handling missing values, scaling, and other transformations.
Note: Ensure you have access to the dataset files as they may not be included in this repository.
## Requirements
The primary libraries required for this project are:
- `pandas`
- `numpy`
- `phik` (for advanced correlation analysis)
- `scipy`
- `matplotlib`
- `seaborn`
- `plotly`
You can install all requirements with:
```bash
pip install pandas numpy phik scipy matplotlib seaborn plotly
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/UznetDev/Competition-for-NT-4th-month.git
cd Competition-for-NT-4th-month
```
2. Install the dependencies listed above.
```sh
pip install -r requirements.txt
```
## Notebook Structure
The notebook follows a structured workflow, outlined as follows:
1. **Setup and Imports**: Imports necessary libraries and sets up the environment.
2. **Data Loading**: Loads the dataset, explores the structure, and checks for missing values.
3. **Feature Engineering and Correlation Analysis**: Uses `phik` and other correlation measures to identify important features.
4. **Visualizations**: Visualizes feature distributions and correlations.
5. **Model Training**: Trains a predictive model and evaluates its performance on the dataset.
6. **Evaluation**: Evaluates the model's performance metrics to gauge its effectiveness in solving the competition's problem.
## Usage
1. Open the notebook `Competition_for_NT_4th_month.ipynb` in Jupyter or Google Colab.
2. Run each cell step-by-step to reproduce the analysis and model training.
3. Modify parameters and experiment with different models to improve performance.
### Running in Google Colab
To run the notebook in Google Colab, open the following link:
[Open in Colab](https://colab.research.google.com/github/UznetDev/Competition-for-NT-4th-month/blob/main/Competition_for_NT_4th_month.ipynb)
## Results
The notebook details the final results, including the accuracy or other performance metrics. Visualizations of the data and feature correlations are provided to illustrate key insights.
## Contributing
Contributions are welcome! Please open an issue to discuss potential changes or improvements.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.