Update README.md

README.md

@@ -56,9 +56,62 @@ The model was evaluated using the following metrics:
 
 ## How to Use
 
-### Installation
+### Input Format
 
-To install the necessary packages, run:
+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
 
-```bash
-pip install -r Requirements.txt
+### 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
+
+#### Example Code:
+
+```python
+from sklearn.externals import joblib
+import pandas as pd
+
+# Load the trained model
+model = joblib.load('random_forest_model.joblib')
+
+# Example input data
+new_data = {
+    'Crop Name': 'apple',
+    'Target Yield': 1200.0,
+    'Field Size': 1.0,
+    'pH (water)': 5.76,
+    'Organic Carbon': 12.9,
+    'Total Nitrogen': 1.1,
+    'Phosphorus (M3)': 1.2,
+    'Potassium (exch.)': 1.7,
+    'Soil moisture': 11.4
+}
+
+# Preprocess the input data
+input_df = pd.DataFrame([new_data])
+
+# Ensure the same columns as in training
+input_df = pd.get_dummies(input_df, columns=['Crop Name'])
+for col in X.columns:
+    if col not in input_df.columns:
+        input_df[col] = 0
+
+# Make predictions
+predictions = model.predict(input_df)
+
+print("Predicted nutrient needs:")
+print(predictions)