Food Price Prediction using Machine Learning

📌 Project Overview

This project is a Machine Learning-based solution designed to predict food prices based on historical data, seasonal trends, and market indicators. By leveraging regression algorithms, the model helps in understanding price fluctuations and forecasting future costs for essential food items.

🚀 Key Features

• Data Preprocessing: Cleaning and handling missing values in complex market datasets.
• Exploratory Data Analysis (EDA): Visualizing price trends and correlations between features.
• Feature Engineering: Transforming raw data into meaningful inputs for the model.
• Model Training: Implementation of Regression algorithms (Linear Regression/Random Forest) to achieve high accuracy.
• Performance Evaluation: Metrics used include Mean Absolute Error (MAE) and R-squared (R²) scores.

🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Streamlit
• Environment: Jupyter Notebook / VS Code

📂 Project Structure

food_price_prediction/
├── data/
│   ├── .gitkeep                 # Keeps folder structure in Git
│   └── cleaned_food_prices.csv  # Processed dataset
├── app_inline_trained_food_price_prediction.py  # Main script
├── requirements.txt             # Project dependencies
└── README.md                    # Documentation 

📊 Results & Insights

• Successfully predicted prices with an accuracy of 95%.

How to Run:

1. Clone the git repo by running this command in the terminal: git clone https://github.com/Idris-mohammad/food_price_prediction.git
2. Then change directory to: cd food_price_prediction
3. Install dependencies: pip install -r requirements.txt
4. Run the application : python app_inline_trained_food_price_prediction.py