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 Application 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
+
+This script includes:
+
+    Loading the models and preprocessor.
+    Defining the categorical and numerical targets.
+    Loading the label encoders.
+    Creating a function make_predictions that processes the input data, makes predictions, and decodes the categorical predictions.
+
+### Inference Procedure
+
+```python
+import pandas as pd
+from joblib import load
+from huggingface_hub import hf_hub_download
+from sklearn.preprocessing import LabelEncoder
+
+# Load models and preprocessor
+preprocessor_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='preprocessor.joblib')
+numerical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='numerical_model.joblib')
+categorical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='categorical_model.joblib')
+
+preprocessor = load(preprocessor_path)
+numerical_model = load(numerical_model_path)
+categorical_model = load(categorical_model_path)
+
+# Define categorical targets
+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)',
+    '1st Application_1',
+    '1st Application - Type fertilizer (1)_3',
+    '1st Application - Type fertilizer (2)_5',
+    '2nd Application_6',
+    '1st Application_21',
+    '1st Application - Type fertilizer (1)_23',
+    '1st Application - Type fertilizer (2)_25',
+    '2nd Application_26',
+    '2nd Application - Type fertilizer (1)_28'
+]
+
+# Define numerical targets
+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)'
+]
+
+# Load label encoders
+label_encoders = {col: load(hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename=f'label_encoder_{col}.joblib')) for col in categorical_targets}
+
+def make_predictions(input_data):
+    # Convert input data to DataFrame
+    input_df = pd.DataFrame([input_data])
+
+    # Preprocess the input data
+    X_transformed = preprocessor.transform(input_df)
+
+    # Predict with numerical model
+    numerical_predictions = numerical_model.predict(X_transformed)
+
+    # Predict with categorical model
+    categorical_predictions_encoded = categorical_model.predict(X_transformed)
+
+    # Decode categorical predictions
+    categorical_predictions_decoded = {}
+    for i, col in enumerate(categorical_targets):
+        le = label_encoders[col]
+        try:
+            categorical_predictions_decoded[col] = le.inverse_transform(categorical_predictions_encoded[:, i])
+        except ValueError as e:
+            categorical_predictions_decoded[col] = ["Unknown"] * len(categorical_predictions_encoded[:, i])
+
+    # Combine numerical and categorical predictions into a dictionary
+    predictions_combined = {col: numerical_predictions[0, i] for i, col in enumerate(numerical_targets)}
+    predictions_combined.update({col: categorical_predictions_decoded[col][0] for col in categorical_targets})
+
+    return predictions_combined
+
+# Example usage
+input_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
+}
+
+predictions = make_predictions(input_data)
+
+print("Predicted Fertilizer Requirements:")
+for col, pred_value in predictions.items():
+    print(f"{col}: {pred_value}")
+