Mastering scikit-learn: A Thread for Beginners!

 1️⃣ What is scikit-learn?

• A powerful Python library for Machine Learning (ML)
• Built on NumPy, SciPy & Matplotlib
• Used for classification, regression, clustering & more!

2️⃣ Installation

pip install scikit-learn

(Make sure you have NumPy & SciPy installed!)

3️⃣ Load a Dataset

from sklearn.datasets import load_iris  
data = load_iris()
print(data.keys()) # View dataset keys

4️⃣ Splitting Data for Training & Testing

from sklearn.model_selection import train_test_split  
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)

5️⃣ Training a Model (RandomForest)

from sklearn.ensemble import RandomForestClassifier  
model = RandomForestClassifier() 
model.fit(X_train, y_train) 

6️⃣ Making Predictions

predictions = model.predict(X_test)  
print(predictions) 

7️⃣ Checking Model Accuracy

from sklearn.metrics import accuracy_score  
print(accuracy_score(y_test, predictions)) 

8️⃣ Other ML Models in scikit-learn:

• LogisticRegression() for classification
• DecisionTreeClassifier() for decision trees
• KMeans() for clustering