ML-Classification

Applying Classification Models To Predict The Type Of DryBeans.

Title of Project: Predicting the type of dry bean based on the dimensions and shape of the dry bean.

Project Plan:

1. Data Preprocessing:
   -----Data Loading: Load the dataset from the provided CSV file.
   -----Data Cleaning: Check for missing values and handle them appropriately.
   -----Data Encoding: Encode variables using Label Encoding to convert in numerical format suitable for models.
   -----Train-Test Split: Split the dataset into training and testing sets for model evaluation.
   

2. Model Building:
   ----- Using various classification models or algorithms to classify the income of an adult in our dataset based on various parameters or features and also evaluating each model’s performance using accuracy metrics classification reports and confusion matrix.
   

   -----Models to be implemented:

    -----Logistic Regression
        -----Suitable for binary classification tasks. 
        -----Provides probabilities for outcomes. 
        -----Interpretable coefficients allow understanding the impact of features on the target variable.
        
    -----Decision Trees
        -----Can handle both numerical and categorical data. 
        -----Intuitive and easy to interpret. 
        -----Automatically handles feature interactions and variable interactions. 
        
    -----Random Forests
        -----Ensemble method combining multiple decision trees for improved performance. 
        -----Reduces overfitting compared to individual decision trees. 
        -----Robust to outliers and noise in the data. 
   
    -----Support Vector Classifier (SVC)
        -----Effective in high-dimensional spaces. 
        -----Versatile due to the choice of different kernel functions. 
        ----- Memory efficient as it uses only a subset of training points in the decision function. 
    
    ----- k-Nearest Neighbors (k-NN)
        ----- Non-parametric method suitable for both classification and regression tasks.
        ----- Simple and intuitive approach based on similarity of data points. 
        ----- Can capture complex patterns in the data.. 
   

3. Next Steps:
   ----- Implement the data preprocessing steps including loading, cleaning, and encoding the dataset.
   ----- Split the dataset into training and testing sets.
   ----- Build and train each classification model using the training data.
   ----- Evaluate the performance of each model using accuracy metrics and classification reports.
   ----- Plotting confusion metrics of each model as a heatmap.
   ----- Select the best-performing model based on results or consider ensemble methods for improved accuracy.