https://github.com/bryanfks-dev/klempoken-analysis
Analysis and forcasting model for Klempoken MSMEs
https://github.com/bryanfks-dev/klempoken-analysis
big-data-analytics data-analysis data-forecast data-visualization
Last synced: about 1 year ago
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Analysis and forcasting model for Klempoken MSMEs
- Host: GitHub
- URL: https://github.com/bryanfks-dev/klempoken-analysis
- Owner: bryanfks-dev
- Created: 2024-12-05T04:17:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-11T02:57:12.000Z (over 1 year ago)
- Last Synced: 2025-02-07T07:13:33.372Z (over 1 year ago)
- Topics: big-data-analytics, data-analysis, data-forecast, data-visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 6.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Klempoken Analysis
Klempoken Analysis is a project aimed at plotting, analyzing, and predicting sales trend for Klempoken MSMEs. This repository provides tools for data preprocessing, analysis, visualization, and forecasting to help Klempoken MSMEs make informed decisions.
## Features
- **Data cleanning**: handles missing values, type casting, and parsing.
- **Visualization**: Generate meaningful plots for better insight.
- **Forecasting Model**: Implement a time series forecasting technique ([Prophet](https://facebook.github.io/prophet/) by Meta)
## Requirements
This program built using python or rather jupyter notebook (version 3.11.7), with several libraries and tools, such as:
- **Pandas** for data manipulation.
- **Matplotlib** and **Seaborn** for data visualization.
- **Prophet** for time series forecasting.
## Setup
1. Clone the repository
```bash
git clone https://github.com/bryanfks-dev/Klempoken-Analysis
cd klempoken-analysis
```
2. Set up a virtual environment (Optional)
```bash
python3 -m venv venv
```
3. Activate the virtual environment (Optional)
```bash
# On MacOS / Linux:
source venv/bin/activate
# On Windows:
.\venv\Scripts\activate
```
4. Install the required depedencies
```bash
pip install -r requirements.txt
```
## Usage
To run the analysis program:
```bash
jupyter execute plot_analysis.ipynb
```
To run the forecast program:
```bash
jupyter execute forecast.ipynb
```