crop-recommendation-ml

implementing logistic regression crop classification model using scikit learn

Crop Recommendation 🌾🤖

A multi‑class classification model that suggests the most suitable crop to sow in a field based on soil and environmental conditions.

**Dataset**	Crop Recommendation Dataset — Kaggle
**Samples**	2 ,200 rows × 7 features
**Target**	22 crop labels (multinomial)
**Model**	Logistic Regression (multinomial)

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1. Features

Column	Description
N, P, K	Soil macro‑nutrient values (kg / ha)
temperature	°C
humidity	%
ph	Soil pH
rainfall	mm
Target → label	Crop name (22 classes)

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2. Exploratory Data Analysis 

Key visualisations include:

• Rainfall vs. crop – box‑plots reveal rainfall requirements
• pH vs. crop – violin‑plots show acidic / alkaline preferences
• Pair‑plot – relationship between N, P, K & pH coloured by crop
• Class distribution – count‑plot ensures no severe imbalance

(see notebook for charts)

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3. Pre‑processing

• Encoded crop names → label_encoded using LabelEncoder
• Train‑test split 80 / 20 with random_state = 42
• No missing values → dataset ready for modelling

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4. Model & Training ⚙️

model = LogisticRegression(
        multi_class="multinomial",
        solver="lbfgs",
        max_iter=500)
model.fit(X_train, y_train)