# Project Title: Iris Dataset Classification using Random Forest Regressor ## Overview This project aims to perform classification on the famous Iris dataset using a Random Forest Regressor machine learning model. The goal is to achieve high accuracy in classifying iris flowers into three different species based on their features: sepal length, sepal width, petal length, and petal width. ## Dataset The Iris dataset used in this project is a well-known dataset available in scikit-learn. It contains 150 samples of iris flowers, each from one of three species: Setosa, Versicolor, and Virginica. The dataset comprises four features (sepal length, sepal width, petal length, and petal width) and their corresponding target labels. ## Project Steps ##Data Cleaning: + Checked for missing values: Ensured that there were no missing values in the dataset. + Removed duplicates: Checked for and removed any duplicate records in the dataset. + Validated data integrity: Examined the dataset for any erroneous or unrealistic values and corrected them as needed. ## Data Visualization: + Performed initial data visualization to gain insights into the dataset. + Visualized the distribution of the different iris species using histograms, scatter plots, and other relevant plots. + Explored the correlations between features using correlation matrices and pair plots. ## Model Selection: Chose Random Forest Regressor as the classification model for this project due to its effectiveness in handling complex datasets and its ability to provide feature importance scores. Hyperparameter Tuning: + Employed hyperparameter tuning techniques to optimize the performance of the Random Forest Regressor model. + Used Randomized Search Cross-Validation (RandomizedSearchCV) to search for the best combination of hyperparameters. ## Model Training and Evaluation: + Split the dataset into training and testing sets. + Trained the Random Forest Regressor model on the training data. + Evaluated the model's performance using various metrics, with a primary focus on accuracy. + Achieved a remarkable accuracy of 97.77%. ## Repository Structure The project repository is organized as follows: ## Data: Contains the Iris dataset used in the project. Notebooks: Jupyter notebooks used for data cleaning, data visualization, model training, and hyperparameter tuning. Scripts: Python scripts for specific functions or utility functions used in the project. Models: Saved trained Random Forest Regressor model(s). Results: Contains project results, including evaluation metrics and visualizations. README.md: This README file, providing an overview of the project and its components. Dependencies To run this project, you'll need the following Python libraries: + NumPy + Pandas + Matplotlib + Seaborn + Scikit-Learn

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