I would suggest to create projects that can :

• Help you to clear your basics
• Help you to demonstrate your skils
• Helps to refer to it whenever needed
• And lastly to impress the recruiters.

Understand that the projects you create should cover most of the basic functionalities of the language and should match with the real world problems.

To impress recruiters and increase your chances of being selected for the data science domain, focus on building projects that showcase your technical skills, creativity, and problem-solving abilities. Here are some project ideas that can make a positive impact:

1. Real-World Data Analysis: Conduct in-depth data analysis on real-world datasets. Choose datasets related to industries or topics of interest, and use data visualization tools to present insights effectively.
2. Machine Learning Models: Develop machine learning models for various tasks, such as classification, regression, or clustering. Implement models using popular libraries like scikit-learn or TensorFlow.
3. Natural Language Processing (NLP): Create projects that involve processing and analyzing natural language data, such as sentiment analysis, text summarization, or language translation.
4. Computer Vision Applications: Build computer vision projects like image classification, object detection, or facial recognition using libraries like OpenCV or TensorFlow.
5. Time Series Analysis: Work on time series datasets and develop predictive models for forecasting future trends or events.
6. Recommender Systems: Design and implement recommender systems that provide personalized recommendations based on user behavior or preferences.
7. Data Visualization Dashboards: Build interactive data visualization dashboards using tools like Tableau or Dash. Present complex data in an intuitive and visually appealing manner.
8. Deep Learning Projects: Create deep learning projects like image generation with Generative Adversarial Networks (GANs), style transfer, or natural language generation.
9. Big Data Analysis: Work with large-scale datasets using distributed computing platforms like Apache Spark or Hadoop.
10. Kaggle Competitions: Participate in Kaggle competitions to solve real-world data science problems and showcase your ranking and performance.
11. End-to-End Projects: Develop end-to-end data science projects that involve data collection, cleaning, exploration, modeling, and deployment.
12. Data Scraping and APIs: Build projects that involve web scraping and utilizing APIs to gather data from various sources.

Remember, the key to impressing recruiters is not just the complexity of the project but also the quality of implementation, your ability to interpret results, and the insights you derive from the data. Focus on projects that align with your interests and demonstrate your expertise in the data science domain. Additionally, document your projects well on platforms like GitHub with clear explanations, code documentation, and visualizations. A well-presented portfolio will make a lasting impression on recruiters during the placement process.