🍋 MangoFruitDDS: Statistics, Analysis, and Prediction

This project focuses on exploring and predicting mango fruit defect detection system (DDS) outcomes using machine learning models. A comprehensive analysis was performed on a dataset sourced from Kaggle, with model comparison based on multiple evaluation metrics.

📊 Dataset

• Source: MangoFruitDDS Dataset on Kaggle
• The dataset contains features related to mango fruit attributes and defect classifications.

🧠 Models Used

The following classification models were applied and compared:

• Logistic Regression
• Ridge Regression
• Lasso Regression
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)

📈 Evaluation Metrics

Models were evaluated using:

• AUC-ROC Curve
• Precision-Recall Curve
• Confusion Matrix
• Box Plots

🏆 Results

• K-Nearest Neighbors (KNN) achieved the highest predictive performance across most evaluation metrics.
• Logistic Regression followed closely, offering strong performance with interpretability.

📌 Key Highlights

• Comparative model analysis for classification on mango defect detection.
• Visual performance insights using AUC-ROC, PR curve, confusion matrix, and box plots.
• Hyperparameter tuning and regularization explored with Ridge and Lasso regression.

🧪 Tech Stack

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
• Jupyter Notebook

🚀 How to Run

1. Clone this repository.
2. Install dependencies using pip install -r requirements.txt.
3. Open the Jupyter notebook and run each cell step-by-step.

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