scikit-learn

1. 1
   Install scikit-learn

   Install scikit-learn using pip. This command will download and install the latest version of the library along with its dependencies.
2. 2
   Load the Iris dataset

   Load the Iris dataset, a classic dataset for classification, using scikit-learn's built-in datasets module.
3. 3
   Split data into training and testing sets

   Split the dataset into training and testing sets using `train_test_split`. This allows you to evaluate the performance of your model on unseen data.
4. 4
   Train a Logistic Regression model

   Create and train a Logistic Regression model using the training data. Logistic Regression is a linear model used for classification tasks.
5. 5
   Make predictions

   Use the trained model to make predictions on the test data.
6. 6
   Evaluate the model

   Evaluate the performance of the model using metrics like accuracy. This provides insight into how well the model is generalizing to new data.

# Install scikit-learn
# pip install scikit-learn

# Load the Iris dataset
# from sklearn.datasets import load_iris
# iris = load_iris()
# X, y = iris.data, iris.target
# print(X.shape)
# print(y.shape)

# Split data into training and testing sets
# from sklearn.model_selection import train_test_split
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# print(X_train.shape)
# print(X_test.shape)

# Train a Logistic Regression model
# from sklearn.linear_model import LogisticRegression
# model = LogisticRegression(random_state=42)
# model.fit(X_train, y_train)

# Make predictions
# y_pred = model.predict(X_test)
# print(y_pred)

# Evaluate the model
# from sklearn.metrics import accuracy_score
# accuracy = accuracy_score(y_test, y_pred)
# print(f'Accuracy: {accuracy}')

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