📝 The Integrated Agricultural Decision Support System (IADSS) is designed to empower farmers with precise, location-specific insights for effective decision-making in agriculture. It integrates weather forecasting, crop prediction, and customized fertilizer recommendations to optimize farming practices.

📊 The project includes the following data files:

• crop_and_fertilizer_data.csv: This file contains information on crop types, soil conditions, and recommended fertilizers.
• weather_forecast.csv: This file provides historical and real-time weather data such as temperature, rainfall, humidity, and wind patterns.

📋 The dataset contains the following data fields:

• District_Name: Name of the district where the crop is cultivated.
• Soil_color: Color of the soil in the cultivation area.
• Nitrogen, Phosphorus, Potassium: Levels of nutrients in the soil.
• pH: pH level of the soil.
• Rainfall, Temperature: Weather conditions at the time of cultivation.
• Crop: Type of crop cultivated.
• Fertilizer: Recommended fertilizer for the specific crop and soil conditions.
• Link: Link to additional information about the recommended fertilizer.

🔍 Explore the data, handle missing values, and preprocess categorical features for model training.

# Load and preprocess the dataset import pandas as pd data = pd.read_csv('crop_and_fertilizer_data.csv') # Data preprocessing steps... 2. Feature Selection and Engineering 🔍 Select relevant features and create new ones to improve model performance. python Copy code # Feature selection and engineering... 3. Model Training and Comparisons ⚙️ Train and compare different models for predicting crop types and recommending fertilizers. python Copy code # Model training and comparisons... from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier # Decision Tree Classifier dt_model = DecisionTreeClassifier() dt_model.fit(X_train, y_train) # Random Forest Classifier rf_model = RandomForestClassifier() rf_model.fit(X_train, y_train) 4. Model Evaluation 📊 Evaluate the trained models using suitable metrics to select the best-performing model. # Model evaluation... from sklearn.metrics import accuracy_score # Model evaluation for Decision Tree Classifier dt_predictions = dt_model.predict(X_test) dt_accuracy = accuracy_score(y_test, dt_predictions) # Model evaluation for Random Forest Classifier rf_predictions = rf_model.predict(X_test) rf_accuracy = accuracy_score(y_test, rf_predictions) 5. Making Predictions 🔮 Use the best-performing model to make predictions on new data. predictions = rf_model.predict(new_data) # Model Selection: Random Forest Classifier ## 🚀 The Random Forest Classifier is chosen over the Decision Tree Classifier for its ensemble learning approach, which improves prediction accuracy and handles overfitting.##

Reduced Overfitting: By aggregating predictions from multiple trees, it mitigates overfitting compared to individual decision trees. Feature Importance: It provides a feature importance score, aiding in understanding significant factors in crop prediction. Usage

💻 To run the project, ensure the required libraries are installed: pip install pandas scikit-learn

📬 For inquiries or collaborations, reach out via:

Email: [mssannitya@gmail.com]