Added two models for crop recommendation

README.md

@@ -1,7 +1,116 @@
 ---
+model_name: Crop Recommendation Model using Random Forest
+tags:
+- tabular-classification
+- sklearn
+- random-forest
+- crop-recommendation
+- agriculture
+library_name: sklearn
+inference: true
+model_description: >
+  This crop recommendation model uses a Random Forest classifier to recommend
+  the best crop
+
+  based on environmental factors such as soil nutrients (N, P, K), pH,
+  temperature, rainfall,
+
+  and humidity. The model is trained to assist farmers in making optimal crop
+  choices based on 
+
+  available agricultural data.
+
+
+  **Features Used:**
+
+  - Nitrogen (N)
+
+  - Phosphorus (P)
+
+  - Potassium (K)
+
+  - pH
+
+  - Temperature
+
+  - Rainfall
+
+  - Humidity
+
+
+  **Example Usage:**
+
+  ```python
+
+  import joblib
+
+  model = joblib.load("crop.pkl")
+  prediction = model.predict([[N, P, K, pH, temperature, rainfall, humidity]])
+  print("Recommended Crop:", prediction)
+metrics:
+- accuracy : 99.86(Random Forest), 99.88(adaboost)
 language:
 - en
-metrics:
-- accuracy
 pipeline_tag: question-answering
----
+---
+---
+models:
+  - model_name: "Crop Recommendation Model using Random Foresst classification"
+    tags:
+      - tabular-classification
+      - sklearn
+      - random-forest
+      - crop-recommendation
+      - agriculture
+    library_name: "sklearn"
+    inference: true
+    model_description: |
+      This crop recommendation model uses a Random Forest classifier to recommend the best crop
+      based on environmental factors such as soil nutrients (N, P, K), pH, temperature, rainfall,
+      and humidity. The model is trained to assist farmers in making optimal crop choices based on 
+      available agricultural data.
+
+      **Features Used:**
+      - Nitrogen (N)
+      - Phosphorus (P)
+      - Potassium (K)
+      - pH
+      - Temperature
+      - Rainfall
+      - Humidity
+
+      **Example Usage:**
+      ```python
+      import joblib
+      model = joblib.load("crop.pkl")
+      # Predict the crop for a given set of environmental features
+      prediction = model.predict([[N, P, K, pH, temperature, rainfall, humidity]])
+      print("Recommended Crop:", prediction)
+      ```
+
+  - model_name: "Crop recommendation model using Adaboost ensemble method"
+    tags:
+      - tabular-classification
+      - sklearn
+      - adaboost
+      - decision-tree
+      - soil-analysis
+    library_name: "sklearn"
+    inference: true
+    model_description: |
+      This soil classification model is built using AdaBoost with a DecisionTreeClassifier as the base estimator. 
+      It classifies soil types based on various properties like pH, nutrient levels, and other soil characteristics.
+    
+      **Algorithm Details:**
+      - Base Learner: DecisionTreeClassifier with max_depth=3
+      - Boosting Method: AdaBoostClassifier with SAMME.R
+      - Number of Estimators: 500
+      - Learning Rate: 0.3
+
+      **Example Usage:**
+      ```python
+      import joblib
+      model = joblib.load("adaboost_model_soil.pkl")
+      prediction = model.predict([[feature1, feature2, ..., featureN]])
+      print("Predicted Soil Type:", prediction)
+      ```