Weather Forecast Data Cleaning using Python for years 2022-2033
⛅ About the project:
Weather report prediction has a great impact on various aspects of human life. In the project, we have been provided with day-wise weather data from Jan 2022 to July 2033 which is predicted data. Which is provided by HiCounselor.
⛅ Objectives of Module 1:
• Handling null values, deletion, or transformation of irrelevant values.
• Datatype transformation, formatting, removing duplicates.
• To Correct the years for a given dataset
• Encoding data into suitable format i.e. UTF-8
• To get a cleaned dataset CSV file
• To analyze Data using SQL problem statements
⛅ Key Features of the Project:
One of the main features of this project is that all data cleaning work was done using Python alone. I used the powerful libraries Pandas and Numpy to handle and manipulate the data, making it ready for analysis. The process involved removing missing values, handling outliers, and transforming the data into a format suitable for analysis.
⛅ Challenges Encountered:
To fix the incorrect year in the Date column - This was the most challenging task in this dataset. In the given raw dataset, the Date column is corrupted. We can see only 2022 throughout the rows. But the given data is day-wise weather attributes from 2022 to July 2033 (predicted data).
However, I was able to overcome this challenge by using Python Construct to efficiently clean the data.
⛅ Analyse Data using MySQL:
In addition to Python, I also used MySQL Workbench to efficiently analyze the data in CSV files. This was a great opportunity for me to expand my knowledge of SQL and its various functions.