This page provides an overview of the first project in the Binary Classification course. Binary classification is a common problem in machine learning where the goal is to predict a binary outcome.
Project Description
The project focuses on a binary classification task where you are given a dataset containing features and a target variable. Your task is to build a model that can accurately predict the target variable based on the features.
Dataset
The dataset used for this project is the Iris dataset, which is a classic dataset in machine learning. It contains 150 samples of iris flowers with four features: sepal length, sepal width, petal length, and petal width.
Steps
- Data Exploration: Understand the data by exploring the distributions of the features.
- Data Preprocessing: Clean and preprocess the data to make it suitable for model training.
- Feature Selection: Select the most relevant features for the classification task.
- Model Selection: Choose a suitable machine learning algorithm for binary classification.
- Training: Train the model using the training dataset.
- Evaluation: Evaluate the model's performance using the test dataset.
- Improvement: Iterate on the model to improve its performance.
Techniques
- Data Visualization: Use plots to visualize the data and identify patterns.
- Statistical Methods: Apply statistical methods to understand the relationships between features.
- Machine Learning Algorithms: Explore various algorithms like Logistic Regression, Decision Trees, and Support Vector Machines.
Resources
Iris Flower