This project focuses on analyzing a weather dataset to uncover patterns and derive insights. It involves comprehensive data preprocessing, exploratory data analysis (EDA), feature engineering, and advanced visualizations using Python. The project demonstrates practical data science techniques to gain a deeper understanding of weather conditions.
- Data Preprocessing: Handling missing values and converting categorical variables into numerical formats.
- Feature Engineering: Created features like
TempRange
(MaxTemp - MinTemp) andAvgHumidity
(average of Humidity9am and Humidity3pm) for enhanced analysis. - Visualizations: Developed scatterplots, boxplots, and correlation heatmaps to reveal trends and relationships.
- Regex Applications: Extracted and cleaned wind direction data using regular expressions.
- Python Libraries: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn, Regex
- Visualization Techniques: Scatterplots, boxplots, heatmaps, and distribution plots.
- Cleaned and enhanced dataset.
- Insightful visualizations to represent weather patterns.
- Features engineered to support advanced data analysis.
- Develop practical data preprocessing and feature engineering skills.
- Explore and visualize key weather metrics to derive actionable insights.
- Apply Python libraries and regex techniques effectively.