Raisin Class Prediction Project

Overview

This project focuses on building machine learning models to predict the class of raisins based on various features such as size, color, texture, etc.

Dataset

The dataset used in this project is the famous Raisin dataset, which contains information about different types of raisins and their characteristics. The dataset is available in the file raisin_data.csv.

Files

• Raisin_Dataset.xlsx: The dataset containing information about different types of raisins.
• Capstone Project-2 (Raisin_Class_Prediction).ipynb: Jupyter Notebook containing the code for data analysis, exploratory data analysis (EDA), outlier analysis, visualization, and building machine learning models.

Data Analysis

• Exploratory data analysis (EDA) is performed to understand the structure and characteristics of the dataset.
• Outlier analysis is conducted to identify and handle outliers in the data.
• Various other analyses are conducted to gain insights into the dataset.

Machine Learning Models

• Machine learning models are built using various algorithms such as logistic regression, decision trees, random forests, etc.
• Hyperparameter tuning is performed to optimize the performance of the models.

Results

• The performance of the machine learning models is evaluated using appropriate metrics such as accuracy, precision, recall, F1-score, etc.
• The predictions of the models are compared with actual raisin classes to assess their effectiveness.