Classifier

Inspiration

Glass is used in numerous industrial applications, from construction and architecture to automotive and consumer electronics. The ability to classify glass types accurately can lead to improved quality control, cost savings, and safety enhancements in these industries.

Glass recycling is an essential part of environmental sustainability efforts. Accurate glass classification can help streamline the recycling process by separating different types of glass effectively, contributing to reduced waste and energy savings.

What it does

The project "Classifier" creates a machine learning model capable of categorizing different types of glass based on their unique properties. It essentially automates the task of classifying glass, providing a reliable and efficient means of identifying and distinguishing between various glass compositions and characteristics.

How we built it

By following the below steps, the project is built that identify and categorize various types of glass based on their properties. Data Collection:

Gather a dataset containing information about different glass samples. This dataset should include features (attributes) that describe the physical and chemical properties of the glass samples, as well as the corresponding class labels indicating the glass types. Data Preprocessing:

Clean and preprocess the dataset to ensure data quality and consistency. This may involve handling missing values, removing duplicates, and addressing outliers. Scale or normalize features if necessary to ensure they have similar ranges. Encode categorical variables, if any, into numerical representations. Data Splitting:

Divide the dataset into two parts: a training set and a testing set. The training set is used to train the machine learning model, while the testing set is used to evaluate its performance.

Model Selection: svm model, logistic regression model, knn model are applied.knn model has high accuracy. So, knn model is selected. Model Training:

Train the selected machine learning model using the training dataset. The model learns to identify patterns and relationships between the input features and the corresponding glass type labels. Model Evaluation:

Assess the model's performance using evaluation metrics such as accuracy, precision, recall, F1-score, and the confusion matrix. This step helps determine how well the model can classify glass types. Hyperparameter Tuning:

Optimize the model's hyperparameters to improve its performance. Techniques like grid search or random search can be employed to find the best hyperparameter combinations. Visualization:

Create visualizations, such as decision boundaries or feature importance plots, to better understand how the model makes classification decisions.