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Machine Learning · Major Project

Crop Yield Prediction for Indian Agriculture

Estimates yield from state, season, crop and rainfall

2-3 weeks build

Overview

Combines public district-level crop production statistics from data.gov.in with seasonal rainfall figures to predict yield per hectare for a given state, season and crop. Tree ensembles are compared with a linear baseline and validated on held-out years, and a bilingual-ready web form lets a user pick a crop and region to see an estimate.

What makes it stand up

  • 01Crop production and rainfall data merged by state, district and season
  • 02Yield per hectare derived and cleaned for outliers
  • 03Validation on held-out years rather than random rows
  • 04Feature importance showing the role of rainfall and season
  • 05Web form built so labels can be translated into regional languages

Module breakdown

  1. 01

    Data merge

    Joins production records with rainfall by region and season.

  2. 02

    Cleaning

    Handles missing areas, units and extreme yields.

  3. 03

    Models

    Linear baseline, random forest and XGBoost.

  4. 04

    Evaluation

    Year-wise error and state-level breakdowns.

  5. 05

    Web app

    Flask API with a React form for estimates.

After this, you will be able to

  • Explain why you validate on unseen years
  • Defend how rainfall features were aligned to crop seasons
  • Demo a yield estimate for a crop and state the examiner chooses

Technology stack

  • Python
  • Pandas
  • scikit-learn
  • XGBoost
  • Flask
  • React

Complete kit

₹6,399₹10,899
-41%
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Delivered in under 48 hours

Level
Major Project
Domain
Machine Learning
Build time
2-3 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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