Update README.md

README.md

@@ -1,14 +1,64 @@
-# Random Forest Regressor for Nutrient Needs Prediction
+---
+license: mit
+datasets:
+- custom
+metrics:
+- mean_squared_error
+- mean_absolute_error
+- r2_score
+model_name: Random Forest Regressor for Crop Nutrient Prediction
+tags:
+- random-forest
+- regression
+- agriculture
+- soil-nutrients
+---
 
-This repository contains a Random Forest Regressor model trained to predict the nutrient needs for various crops based on features such as target yield, field size, pH, organic carbon, total nitrogen, phosphorus, potassium, and soil moisture.
+# Random Forest Regressor for Crop Nutrient Prediction
 
-## Model Description
+## Overview
 
-The model is a Random Forest Regressor, which is an ensemble learning method for regression that operates by constructing multiple decision trees during training and outputting the average of the predictions of the individual trees. This approach helps in improving the model's accuracy and robustness.
+This model predicts the nutrient needs (Nitrogen, Phosphorus, Potassium) for various crops based on features like crop type, target yield, field size, and soil properties. It is trained using a Random Forest Regressor.
 
 ## Training Data
-## Usage
 
-### Loading the Model
+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
 
-To use the model, first load it using `joblib`:
+The target variables are:
+- Nitrogen (N) Need
+- Phosphorus (P2O5) Need
+- Potassium (K2O) Need
+
+## Model Training
+
+The model was trained using a Random Forest Regressor. Below are the steps taken for training:
+
+1. Data preprocessing: handling missing values, scaling numerical features, and one-hot encoding categorical features.
+2. Splitting the dataset into training and testing sets.
+3. Training the Random Forest model on the training set.
+4. Evaluating the model on the test set.
+
+## Evaluation Metrics
+
+The model was evaluated using the following metrics:
+- Mean Squared Error (MSE)
+- Mean Absolute Error (MAE)
+- R-squared (R2) Score
+
+## How to Use
+
+### Installation
+
+To install the necessary packages, run:
+
+```bash
+pip install -r requirements.txt