Leveraging powerful Python libraries such as Pandas, Numpy, Matplotlib, Seaborn to get the required insights and visualization from the raw data, which helped the company understand the data more effectively and make data-driven decisions confidently.
1.Gathered required raw weather data from external sources.
2.Imported the data into Jupyter Notebook using Pandas functions.
3.Cleaned the data by checking for null values, incorrect data types, irregular index, and irregular column names.
4.Performed basic Exploratory Data Analysis (EDA) to gather relevant trends, behavior, and insights.
5.Used various graphs like scatter plots, bar charts, box plots, pie charts, and histograms to visualize the data and identify hidden trends and patterns.