Explore the financial horizons of Kathmandu's vital agricultural hub with freshForecast. Dive into a technological ensemble of rich historical data and acute machine learning precision, crafting not only a forecast but a tangible guide through the ebbs and flows of the Kalimati market's future prices.
freshForecast is an insightful deep learning project that casts light on the intricate price dynamics of the prominent Kalimati Fruits and Vegetables Market, which significantly satisfies 60-70% of Kathmandu Valley's demand for agricultural produce. This project harnesses the predictive power of Long Short-Term Memory (LSTM) networks— a specialized form of Recurrent Neural Networks (RNNs) — to analyze and forecast fruit and vegetable prices, utilizing a comprehensive dataset spanning from 2013 to 2021. The ambition here is not merely to visualize historical data but to adeptly predict future market prices, providing a vital tool for researchers, market analysts, and policymakers in crafting informed strategies and decisions in the agricultural market domain.
This project utilizes an open-source dataset from Open Data Nepal, available at: Kalimati Tarkari Dataset . The dataset is employed here strictly for educational and research purpose only.
Ensure you have Python installed on your machine. If not, download and install it from Python's official site.
Follow these steps to get the project up and running on your local machine:
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Clone the Repository
git clone https://github.com/suyogkad/freshForecast.git cd freshForecast -
Install Requirements
Make sure to create a virtual environment before installing dependencies.
python -m venv venv source venv/bin/activate # Linux/macOS venv\Scripts\activate # Windows pip install -r requirements.txt
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Run the Flask app
python app.py
Now, navigate to http://127.0.0.1:5000/ in your browser to access the application.
Users can leverage freshForecast to meticulously analyze the fruits and vegetables prices from 2013 to 2021 in Kalimati, Kathmandu, and also to predict future prices. By selecting a specific fruit or vegetable, users can gain insights into its price predictions, aiding in more informed decision-making or deriving valuable insights for research and study purposes.
While the utilized dataset is open and accessible from Open Data Nepal, this project's codebase is not open source and is intended solely for educational and academic viewing. Usage, modification, or distribution of the code requires explicit permission from the author. View LICENSE.