Python data analysis completed in 6 steps!
1. Import libraries, typically including libraries for data reading, visualization, and machine learning.
2. Data input, which can be from local CSV or Excel files, or datasets included with machine learning packages.
3. Data processing, usually involves examining the overall situation of the data, handling duplicates and missing values, etc.
4. Data visualization, which facilitates a quick understanding of data distribution, typically using matplotlib to draw various graphs.
5. Feature engineering, selecting more important data columns as inputs, including encoding categorical data and normalizing data.
6. Model training and evaluation, including splitting the dataset, building models for training, and evaluating model performance.
Tools for data processing include: SQL, Python, Pyspark, SAS, R;
Data analysis and data science: SAS, Python Pandas, Python Sklearn, XGBoost;
Data visualization: Power BI, Tableau, Looker.
Python is a star-level analysis tool in the field of data analysis, providing a complete solution for data acquisition, cleaning, analysis, and modeling. Let’s explore how to use ChatGPT to generate Python code for data acquisition and analysis:
1. ChatGPT teaches me to prepare the development environment.
2. Tell ChatGPT my requirements.
3. ChatGPT teaches me to test and optimize the code.
4. Execute the optimized code to automatically acquire data and save it to a file every day at 15:00.
5. View the analysis results.