week1 Crop & Fertilizer Recommendation System Using ML

Crop Recommendation System

Overview

This project involves analyzing agricultural data to develop a crop recommendation system. Using various machine learning and data analysis techniques, the system aims to recommend the most suitable crops based on specific environmental and soil parameters. The dataset used is Crop_recommendation.csv.

Features

• Data analysis and visualization using Python libraries.
• Insights into the dataset, such as crop suitability based on environmental conditions.
• Recommendations for optimal crop growth.

Technologies Used

• Python: Programming language for data manipulation and analysis.
• NumPy: For numerical computations.
• pandas: For data manipulation and preprocessing.
• matplotlib and seaborn: For data visualization.
• scikit-learn: For machine learning models (if applicable).

Installation

1. Clone the repository:

   git clone https://github.com/your-repository-link.git

2. Navigate to the project directory:

   cd crop-recommendation-system

3. Install the required dependencies:

   pip install -r requirements.txt

Dataset

The dataset Crop_recommendation.csv includes information on various factors influencing crop growth, such as:

• Soil pH
• Rainfall
• Temperature
• Humidity
• Nutrient levels

Usage

1. Load the dataset:

   import pandas as pd
   crop = pd.read_csv("Dataset/Crop_recommendation.csv")

2. Perform exploratory data analysis (EDA) to understand the dataset:

   crop.head()  # View the first 5 rows
   crop.shape   # Get dataset dimensions

3. Use visualization tools like seaborn and matplotlib to uncover patterns and trends.
4. Implement machine learning models (if included) to predict suitable crops based on input parameters.

Example Output

• Recommendations for the most suitable crop based on user input.
• Visualizations highlighting correlations and trends.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Dataset Source: Provide the link or credit to the dataset source.
• Inspiration for this project came from the need to support farmers in making data-driven decisions.