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A Python application for predicting commodity prices (e.g., Pulses, Bread) based on state, city, year, and month using a Linear Regression model. Trained on over 1 million government dataset entries, featuring efficient data processing and prediction capabilities.

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🌟 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.


πŸ“œ 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.


πŸš€ Features

  • πŸ“Š Predict prices of commodities such as apples, ghee, and more.
  • 🌍 Location-based predictions: state and city level.
  • πŸ—“οΈ Time-specific predictions: year and month wise.
  • ⚑ Efficient data processing with Pandas for managing large datasets.
  • πŸ“ˆ Robust Linear Regression Model trained on comprehensive datasets.

πŸ› οΈ 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.

🧠 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.

About

A Python application for predicting commodity prices (e.g., Pulses, Bread) based on state, city, year, and month using a Linear Regression model. Trained on over 1 million government dataset entries, featuring efficient data processing and prediction capabilities.

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