🎯 Project Objective
To introduce Machine Learning (ML) in Python and build a simple ML model for predictions.
This project demonstrates:
- Using scikit-learn for ML tasks
- Data preprocessing and splitting
- Training and testing models
- Making predictions from real-world datasets
Project: Simple Machine Learning Model
Project Description
The ML Intro project involves creating a predictive model using Python. Example tasks:
- Predicting house prices based on features
- Classifying iris flowers based on petal/sepal dimensions
- Predicting student pass/fail based on scores
Use Case Example: Using the classic Iris dataset to classify flower species.
Python Example Code – Iris Classification
# Import libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
# Load dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))
# Predict for a new sample
sample = [[5.1, 3.5, 1.4, 0.2]] # Example input
prediction = iris.target_names[model.predict(sample)[0]]
print("\nPredicted Species:", prediction)
✅ Output:
- Model accuracy and classification report
- Prediction of a new sample
✅ Key Features
- Load and explore datasets
- Split data into training and testing sets
- Train a machine learning model
- Evaluate model performance
- Make predictions for new input
Leave a Reply