The Iris Flowers Classification project aims to develop a machine learning model that accurately classifies different species of iris flowers based on their sepal and petal measurements. The dataset used in this project is the famous "Iris" dataset, which contains samples of three iris species: Setosa, Versicolor, and Virginica. The project utilizes various machine learning algorithms and techniques to achieve high classification accuracy.
Iris flowers are widely known for their distinct species, and the classification of these species is crucial for botanical research and gardening. In this project, we aim to build a model that can accurately classify iris flowers based on their physical attributes, namely the length and width of sepals and petals.
The Iris dataset contains 150 samples, with 50 samples for each of the three iris species. Each sample has four features: sepal length, sepal width, petal length, and petal width. The dataset is labeled with the corresponding species for each sample.
Before feeding the data into machine learning models, we performed data preprocessing tasks such as handling missing values, feature scaling, and label encoding. Additionally, I split the dataset into training and testing sets to evaluate the model's performance accurately.
EDA was conducted to gain insights into the dataset's characteristics and the relationships between features. Visualizations like scatter plots, histograms, and correlation matrices were used to better understand the data.
I experimented with multiple classification algorithms, including but not limited to:
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Logistic Regression
Each model was trained using the training dataset and evaluated on the test dataset using metrics such as accuracy, precision, recall, and F1-score. Hyperparameter tuning was performed to optimize the models' performance.
After evaluating the models, we found that the Random Forest algorithm performed the best, achieving an accuracy of approximately 95%. The decision tree model also showed competitive results but had a slightly higher tendency to overfit.
In conclusion, the Iris Flowers Classification project successfully developed a machine learning model that can accurately classify iris flowers into their respective species based on their sepal and petal measurements. The project demonstrates the importance of data preprocessing, model selection, and evaluation for creating effective classification models.
For future work, we could explore other advanced machine learning algorithms, implement ensemble methods to further improve accuracy, and consider feature engineering to enhance the model's performance. Additionally, expanding the dataset with more samples and species could lead to broader applications in the field of botany and plant species identification