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ML · B.Tech 3rd / 4th year · mini or major · 6–8 weeks guided

Crop Yield Forecasting from Weather and Soil Parameters

Regression major project: public agri + weather features, model comparison, error by district, and a planner UI (“what if rainfall drops 20%”). Maps if the team can handle GeoJSON; tables otherwise.

Student project kit

₹14,999/-₹8,999/-

1–4 students · 6–8 weeks guided. College title can be adapted on the counselling call.

Audience

Who it is for

ML / data teams who want regression done with baselines, not only classification.

Pick crop + district, see forecast vs last year, error band, and a one-page PDF brief.

Branches

CSEITAI & DSECE

Team size: 1–4 students

Problem

Existing vs proposed

Yield talk is anecdotal. Students often dump one Kaggle CSV into LinearRegression with no error analysis.

Feature set (rain, temp, soil, area), train/test by year, RMSE/MAE vs naive baseline, residual plots, what-if slider for rainfall.

Build

Modules

Pythonpandasscikit-learnFastAPIReactoptional Folium/Map

  1. 1. Data

    • Join weather and yield
    • Missing years, outliers

  2. 2. Models

    • Naive last-year baseline
    • Ridge / RF / GBDT compare

  3. 3. Scenarios

    • Rainfall slider
    • Error band

  4. 4. Brief

    • District PDF
    • Limits: not a mandi price predictor

Demo

What you can show

  • Baseline before ML
  • What-if rainfall
  • Geographic slice of error

Viva

Panel topics

  • Why RMSE vs MAE
  • Distribution shift across years

Deliverables

Included with the kit

  • Complete source code (ZIP + GitHub-style repo)
  • Database schema and sample data / dataset notes
  • README.md — how to run, stack, modules, and demo path (Windows + college lab)
  • Viva question bank with short answers
  • Two mentor reviews: architecture, then final demo

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