https://github.com/jimmymugendi/luxdev-week-2-boot-camp

https://github.com/jimmymugendi/luxdev-week-2-boot-camp

correlation-analysis eda matplotlib numpy pandas seaborn-plots visualizations

Last synced: about 7 hours ago
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• Host: GitHub
• URL: https://github.com/jimmymugendi/luxdev-week-2-boot-camp
• Owner: Jimmymugendi
• Created: 2024-08-10T11:54:55.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-10T12:20:13.000Z (3 months ago)
• Last Synced: 2024-08-10T13:34:20.733Z (3 months ago)
• Topics: correlation-analysis, eda, matplotlib, numpy, pandas, seaborn-plots, visualizations
• Language: Jupyter Notebook
• Homepage:
• Size: 738 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# LuxDev-week-2-boot-camp

 Week 2: EDA Assignment on the Weather Dataset Analysis

Perform Exploratory Data Analysis (EDA) to uncover interesting patterns, insights, and potential anomalies within the Weather dataset that we used for week 1 Assignment.

Please address the following questions and tasks in your analysis:

1. Data Overview and Cleaning:

* What are the key characteristics of the dataset? (e.g., number of records, features, data types)

* Identify and handle any missing or null values. Describe your approach and reasoning.

* Check for and address any duplicate records.

2. Statistical Summary:

* Provide a statistical summary of the dataset (mean, median, standard deviation, etc.) for numerical features.

* Identify and describe any significant outliers in the data.

3. Data Visualization:

* Create visualizations to show the distribution of key weather parameters (e.g., temperature, humidity, wind speed).

* Plot time series graphs to visualize trends over time. Highlight any notable patterns or seasonal variations.

* Create correlation matrices and heatmaps to identify relationships between different weather parameters.

4. Weather Patterns and Trends:

* Analyze and describe any trends or patterns you observe in the data. For instance, how do temperature and humidity vary across different seasons or months?

* Investigate any anomalies or unusual patterns in the data. What might be the reasons for these anomalies?

5. Insights and Conclusions:

* Summarize the key insights you have gained from your EDA. What are the most interesting or surprising findings?

* How can these insights be useful for weather prediction or other practical applications?

6. Recommendations for Further Analysis:

* Suggest areas for further analysis or additional data that might be useful to explore.

Please present your findings in a well-organized report, including both written explanations and visualizations. Use appropriate libraries (e.g., pandas, matplotlib, seaborn) and ensure your code is well-documented and reproducible