Merge pull request #683 from adi271001/cheese-classification

Cheese classification

Cheese Classification/Model/README.md

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+# Cheese Type Classification - Models and Results
+
+## Table of Contents
+
+- [Models](#models)
+- [Results](#results)
+- [Conclusion](#conclusion)
+- [Signature](#signature)
+
+## Models
+
+The project explores the following machine learning models to classify different types of cheese:
+
+### 1. K-Nearest Neighbors (KNN)
+
+**Description**: 
+KNN is a simple, instance-based learning algorithm where classification is based on the majority vote of the nearest neighbors.
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_1.png)
+
+### 2. Logistic Regression
+
+**Description**: 
+Logistic Regression is a linear model for binary classification that uses a logistic function to model the probability of a certain class.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_2.png)
+
+### 3. Decision Tree
+
+**Description**: 
+Decision Trees are non-parametric supervised learning methods used for classification. The model predicts the target variable by learning simple decision rules inferred from the data features.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_3.png)
+
+### 4. Support Vector Machine (SVM)
+
+**Description**: 
+SVM is a supervised learning model that analyzes data for classification and regression analysis. It finds the hyperplane that best divides a dataset into classes
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_4.png)
+
+### 5. Random Forest
+
+**Description**: 
+Random Forest is an ensemble learning method that operates by constructing multiple decision trees and outputting the class that is the mode of the classes of individual trees.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_5.png)
+
+### 6. Gradient Boosting
+
+**Description**: 
+Gradient Boosting is an ensemble learning technique that builds models sequentially. Each new model attempts to correct errors made by the previous model.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_6.png)
+
+### 7. AdaBoost
+
+**Description**: 
+AdaBoost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It adjusts the weights of misclassified instances to focus on hard-to-classify cases.
+
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_7.png)
+
+### 8. Extra Trees
+
+**Description**: 
+Extra Trees is similar to Random Forest but with more randomness in node splitting, reducing variance and improving performance.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_8.png)
+
+### 9. Naive Bayes
+
+**Description**: 
+Naive Bayes classifiers are probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_9.png)
+
+### 10. XGBoost
+
+**Description**: 
+XGBoost is an optimized gradient boosting framework that is efficient and performs well on structured data.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_10.png)
+
+### 11. CatBoost
+
+**Description**: 
+CatBoost is a gradient boosting algorithm that handles categorical features automatically and efficiently, often providing high accuracy with minimal parameter tuning.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_11.png)
+
+## Results
+
+The accuracy results for each model are as follows:
+
+- **K-Nearest Neighbors**: 0.62
+- **Logistic Regression**: 0.633
+- **Decision Tree**: 0.713
+- **Support Vector Machine**: 0.647
+- **Random Forest**: 0.72
+- **Gradient Boosting**: 0.767
+- **AdaBoost**: 0.727
+- **Extra Trees**: 0.70
+- **Naive Bayes**: 0.64
+- **XGBoost**: 0.727
+- **CatBoost**: 0.74
+
+## Conclusion
+
+After evaluating various machine learning models, it is evident that ensemble methods such as Gradient Boosting, CatBoost, and Random Forest perform significantly better than single classifiers like K-Nearest Neighbors or Logistic Regression. These models effectively capture complex relationships within the data, leading to higher classification accuracy.
+
+- **Best Performing Models:** Gradient Boosting and CatBoost achieved the highest accuracy scores, indicating robust predictive performance.
+- **Important Features:** Features such as fat content, moisture content, and aging time were consistently found to be the most influential in classifying cheese types.
+
+## Signature
+
+Aditya D
+* GitHub: [adi271001](https://www.github.com/adi271001)
+* LinkedIn: [Aditya D](https://www.linkedin.com/in/aditya-d-23453a179/)
+* Topmate: [Aditya D](https://topmate.io/aditya_d/)
+* Twitter: [@ADITYAD29257528](https://x.com/ADITYAD29257528)