I want to ask for a project recommendation related to data science/ML/AI/Cloud related for a portfolio made?

1. Customer Segmentation

Objective: Use clustering algorithms to segment customers based on their purchasing behavior.

Steps:

• Collect or find a dataset that includes customer purchase history and possibly demographic information.

• Perform exploratory data analysis (EDA) to understand the data.

• Preprocess the data, handling any missing values and outliers.

• Use clustering algorithms like K-means, hierarchical clustering, or DBSCAN to segment the customers.

• Analyze the results and create visualizations to show the different customer segments.

• Interpret the results and propose strategies to target each customer segment.

2. Time Series Forecasting for Stock Prices

Objective: Predict future stock prices using time series analysis.

Steps:

• Collect historical stock price data.

• Perform EDA to understand the trends and patterns in the data.

• Preprocess the data, handling any missing values and outliers.

• Use time series forecasting models like ARIMA, SARIMA, or LSTM to predict future stock prices.

• Evaluate the model's performance using appropriate metrics.

• Visualize the predictions and compare them to the actual stock prices.

3. Sentiment Analysis for Product Reviews

Objective: Analyze customer reviews to determine the sentiment towards a product.

Steps:

• Collect customer reviews from websites like Amazon, Yelp, etc.

• Perform EDA to understand the data.

• Preprocess the text data, handling any missing values and outliers.

• Use natural language processing (NLP) techniques to extract features from the text.

• Use machine learning models like Naive Bayes, SVM, or deep learning models like LSTM to predict the sentiment.

• Evaluate the model's performance using appropriate metrics.

• Visualize the results and create a dashboard to show the sentiment analysis results.

4. Serverless Image Processing Pipeline

Objective: Create a serverless pipeline to process images uploaded to a cloud storage bucket.

Steps:

• Set up a cloud storage bucket to store the images.

• Create a serverless function that gets triggered when an image is uploaded to the bucket.

• Write the code for the serverless function to process the image (e.g., resize, filter, etc.).

• Test the pipeline by uploading images to the bucket and checking the processed images.

• Monitor the performance and costs of the serverless pipeline.