OIBSIP_domain_task_no_1

Problem Statement :

The Iris flower dataset is a classic and simple multivariate dataset often used for classification problems in machine learning. The challenge is to build a predictive model that can accurately classify an Iris flower into one of the three species — Setosa, Versicolor, or Virginica — based solely on its physical dimensions: sepal length, sepal width, petal length, and petal width.

Objective :

The primary goal of this project is to build a machine learning model that can classify Iris flowers into three species Iris Setosa, Iris Versicolor, and Iris Virginica based on four key features: sepal length, sepal width, petal length, and petal width. This is a fundamental classification problem in supervised learning and serves as an ideal introduction to machine learning.

Steps Performed :

1. Data Loading

Loaded the Iris dataset using sklearn.datasets and converted it into a DataFrame for easier manipulation.

2. Exploratory Data Analysis (EDA)

Analyzed the structure and distribution of data using descriptive statistics and visualizations such as:

• Histograms
• Pairplots
• Correlation heatmaps
• Box plots

3. Data Preprocessing

• Checked for missing values
• Encoded the target labels using LabelEncoder
• Split the data into training and testing sets

4. Model Training

Trained multiple classification models:

• Logistic Regression
• Decision Tree Classifier
• Support Vector Machine (SVM)
• Random Forest Classifier
• GuassianNB
• Xgboost

5. Model Evaluation

Evaluated each model using:

• Accuracy Score
• Confusion Matrix
• Classification Report

6. Visualization

Visualized classification results, confusion matrix, and decision boundaries (if applicable).

Tools and Libraries Used :

• Programming Language: Python
• Jupyter Notebook

Libraries:

• pandas, numpy – data manipulation
• matplotlib, seaborn – data visualization
• sklearn – machine learning (model training, evaluation, preprocessing)

Dataset Description :

Source: Kaggle

Features:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)

Target Classes:

• Iris Setosa
• Iris Versicolor
• Iris Virginica Total Samples: 150

Outcomes :

1. Successfully built a machine learning model to classify Iris flowers with over 95% accuracy.
2. Random Forest Classifier showed the best performance with balanced precision and recall.
3. Gained hands-on experience in the complete machine learning workflow: data preprocessing, training, tuning, and evaluating.
4. The project also enhanced skills in Python, data visualization, and model interpretability.

Conclusion :

This project solidified understanding of classification problems using machine learning. The Iris dataset proved to be an excellent starting point, offering clean and well-structured data. By comparing multiple models, we learned the importance of evaluation metrics and model selection based on business needs or performance criteria.