🌟 Commodities Price Prediction 🌟

A Data Prediction Application using Python, Pandas, and Scikit-learn
Predict the prices of various commodities like apples, ghee, and more based on state, city, year, and month.

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📜 Overview

The Commodities Price Prediction application leverages Linear Regression to predict commodity prices accurately. With over 1 million data entries from reliable government datasets, this application offers state-of-the-art predictions to help stakeholders make informed decisions.

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🚀 Features

• 📊 Predict prices of commodities such as apples, ghee, and more.
• 🌍 Location-based predictions: state and city level.
• 🗓️ Time-specific predictions: year and month wise.
• ⚡ Efficient data processing with Pandas for managing large datasets.
• 📈 Robust Linear Regression Model trained on comprehensive datasets.

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🛠️ Tech Stack

• Programming Language: Python 🐍
• Libraries:
  • pandas: For efficient data manipulation.
  • scikit-learn: To build and train the Linear Regression model.
• Dataset: Over 1 million entries from government datasets.

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🧠 How It Works

1. Data Collection
   Government datasets containing state, city, year, month, and price data are loaded and processed.

2. Data Preprocessing

   • Cleaning and structuring the data using Pandas and One Hot Encoding.
   • Handling missing values and preparing it for training.

3. Model Training

   • A Linear Regression model is trained on the processed data.
   • The model learns to predict prices based on the input features: state, city, year, and month.

4. Prediction

   • The trained model predicts the price of a commodity based on user-defined parameters.