scikit-learn

Collect labeled dataset of spam and non-spam emails.

Preprocess text data using techniques like TF-IDF or CountVectorizer.

Train a classifier (e.g., Naive Bayes, Logistic Regression) on the training data.

Evaluate the classifier's performance on the test data.

Deploy the trained model to filter incoming emails.

Collect a labeled dataset of images with different objects.

Extract features from images using techniques like SIFT or CNNs.

Train a classifier (e.g., SVM, Random Forest) on the training data.

Evaluate the classifier's performance on the test data.

Use the trained model to recognize objects in new images.

Collect customer data (e.g., purchase history, demographics).

Preprocess the data and select relevant features.

Apply a clustering algorithm (e.g., k-Means, DBSCAN) to group customers.

Analyze the characteristics of each segment.

Develop targeted marketing strategies for each segment.

Collect historical stock price data.

Engineer features such as moving averages, RSI, and MACD.

Train a regression model (e.g., Linear Regression, Random Forest) on the training data.

Evaluate the model's performance on the test data.

Use the trained model to predict future stock prices.

Collect patient data (e.g., demographics, genetic information).

Preprocess the data and select relevant features.

Train a regression model (e.g., Linear Regression, Random Forest) on the training data.

Evaluate the model's performance on the test data.

Use the trained model to predict drug response for new patients.