Create README.md

README.md

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+---
+license: mit
+datasets:
+  - custom
+metrics:
+  - mean_squared_error
+  - mean_absolute_error
+  - r2_score
+model_name: Fertilizer Recommendation System
+tags:
+  - random-forest
+  - regression
+  - multioutput
+  - classification
+  - agriculture
+  - soil-nutrients
+---
+
+# Fertilizer Recommendation System
+
+## Overview
+
+This model predicts the fertilizer requirements for various crops based on input features such as crop type, target yield, field size, and soil properties. It utilizes a combination of Random Forest Regressor and Random Forest Classifier to predict both numerical values (e.g., nutrient needs) and categorical values (e.g., fertilizer application instructions).
+
+## Training Data
+
+The model was trained on a custom dataset containing the following features:
+
+- Crop Name
+- Target Yield
+- Field Size
+- pH (water)
+- Organic Carbon
+- Total Nitrogen
+- Phosphorus (M3)
+- Potassium (exch.)
+- Soil moisture
+
+The target variables include:
+
+**Numerical Targets**:
+- Nitrogen (N) Need
+- Phosphorus (P2O5) Need
+- Potassium (K2O) Need
+- Organic Matter Need
+- Lime Need
+- Lime Application - Requirement
+- Organic Matter Application - Requirement
+- 1st Application - Requirement (1)
+- 1st Application - Requirement (2)
+- 2nd Application - Requirement (1)
+
+**Categorical Targets**:
+- Lime Application - Instruction
+- Lime Application
+- Organic Matter Application - Instruction
+- Organic Matter Application
+- 1st Application
+- 1st Application - Type fertilizer (1)
+- 1st Application - Type fertilizer (2)
+- 2nd Application
+- 2nd Application - Type fertilizer (1)
+
+## Model Training
+
+The model was trained using the following steps:
+
+1. **Data Preprocessing**:
+   - Handling missing values
+   - Scaling numerical features using `StandardScaler`
+   - One-hot encoding categorical features
+
+2. **Modeling**:
+   - Splitting the dataset into training and testing sets
+   - Training a `RandomForestRegressor` for numerical targets using a `MultiOutputRegressor`
+   - Training a `RandomForestClassifier` for categorical targets using a `MultiOutputClassifier`
+
+3. **Evaluation**:
+   - Evaluating the models using the test set with metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) Score for regression, and accuracy for classification.
+
+## Evaluation Metrics
+
+The model was evaluated using the following metrics:
+
+- Mean Squared Error (MSE)
+- Mean Absolute Error (MAE)
+- R-squared (R2) Score
+- Accuracy for categorical targets
+
+## How to Use
+
+### Input Format
+
+The model expects input data in JSON format with the following fields:
+
+- "Crop Name": String
+- "Target Yield": Numeric
+- "Field Size": Numeric
+- "pH (water)": Numeric
+- "Organic Carbon": Numeric
+- "Total Nitrogen": Numeric
+- "Phosphorus (M3)": Numeric
+- "Potassium (exch.)": Numeric
+- "Soil moisture": Numeric
+
+### Preprocessing Steps
+
+1. Load your input data.
+2. Ensure all required fields are present and in the expected format.
+3. Handle any missing values if necessary.
+4. Scale numerical features based on the training data.
+5. One-hot encode categorical features (if applicable).
+
+### Inference Procedure
+
+```python
+from joblib import load
+import pandas as pd
+
+# Load the preprocessor and trained models
+preprocessor = load('preprocessor.joblib')
+numerical_model = load('numerical_model.joblib')
+categorical_model = load('categorical_model.joblib')
+
+# Example input data
+new_data = {
+    'Crop Name': 'maize(corn)',
+    'Target Yield': 3600.0,
+    'Field Size': 1.0,
+    'pH (water)': 6.1,
+    'Organic Carbon': 11.4,
+    'Total Nitrogen': 1.1,
+    'Phosphorus (M3)': 1.8,
+    'Potassium (exch.)': 3.0,
+    'Soil moisture': 20.0
+}
+
+# Preprocess the input data
+input_df = pd.DataFrame([new_data])
+input_transformed = preprocessor.transform(input_df)
+
+# Make numerical predictions
+numerical_predictions = numerical_model.predict(input_transformed)
+
+# Make categorical predictions
+categorical_predictions = categorical_model.predict(input_transformed)
+
+# Decode categorical predictions
+label_encoders = {col: load(f'label_encoder_{col}.joblib') for col in categorical_targets}
+categorical_predictions_decoded = {col: label_encoders[col].inverse_transform(categorical_predictions[:, i]) for i, col in enumerate(categorical_targets)}
+
+# Combine predictions into a single dictionary
+predictions_combined = {**{col: numerical_predictions[0, i] for i, col in enumerate(numerical_targets)}, **categorical_predictions_decoded}
+
+print("Predicted Fertilizer Requirements:")
+for col, pred_value in predictions_combined.items():
+    print(f"{col}: {pred_value}")