Dataset Classification Using Three Different Algorithms

Objective:

The objective of this project is to download a classification dataset from Kaggle and apply three different classification algorithms to predict a target variable.

Steps:

Dataset Selection: Choose a classification dataset from Kaggle with a target variable suitable for classification tasks.

Data Exploration and Preprocessing: Explore the dataset, handle missing values, encode categorical variables, and scale numerical features if necessary.

Model Selection:

Select three classification algorithms $($e$.$g$.,$ Logistic Regression, Decision Trees, SVM$).$

Model Training and Evaluation:

Divide the dataset into training and testing sets.

Train each selected algorithm on the training data.

Evaluate the trained models on the testing data using appropriate evaluation metrics $($e$.$g$.,$ accuracy, precision, recall$).$

Comparison and Analysis:

Compare the performance of the three algorithms based on evaluation metrics.

Deliverables:

Jupyter Notebook or Python script containing code for data preprocessing, model training, and evaluation.

PDF Documentation summarizing the dataset, preprocessing steps, chosen algorithms, evaluation results, and analysis.

The dataset or a link to the dataset.

Every student should present their work and be prepared to demonstrate a solid understanding of the code.