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Objective:
- Analyze climate change using temperature data and visualize trends over time.
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Data Import and Visualization:
- Imported necessary libraries (Matplotlib, NumPy, Seaborn, scikit-learn).
- Loaded NASA's climate change dataset for 30 years (1951-1980) and created a scatter plot.
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Linear Regression with NumPy:
- Performed linear regression using NumPy's polyfit function.
- Plotted the regression line on the scatter plot.
- Computed and displayed the linear equation parameters.
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Linear Regression with scikit-learn:
- Utilized scikit-learn's LinearRegression model for the same analysis.
- Generated a scatter plot with the regression line.
- Displayed the linear equation parameters using scikit-learn.
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Seaborn Visualization:
- Used Seaborn to visualize a larger dataset covering years 1882 to 2014.
- Plotted scatter points and a regression line for better readability.
- Compared actual temperature values with predicted values.
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Analysis Insights:
- Scatter Points: Represented yearly temperature differences from a reference point.
- Red Line (Regression Line): Indicated the overall trend in temperature changes.
- Comparison of Actual vs. Predicted Values: Highlighted the model's accuracy.
- Observation of Data: Noted a deviation from predicted values in recent years.
- Significance of Deviation: Suggested potential acceleration in climate change.
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Implications:
- The analysis provides insights into historical temperature trends.
- Deviation in recent years may indicate accelerating climate change.
- Understanding these trends is crucial for addressing climate-related challenges.
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Conclusion:
- The project combines data analysis and visualization to understand climate change trends.
- Linear regression models help identify patterns and deviations in temperature data.
- Insights from the analysis contribute to ongoing climate change discussions and actions.
- More details of this project in ipynb file https://github.com/ArfaKhalid/Climate-Change-Analysis-Time-Series-Forecasting-in-ML/blob/main/Climate_change_anlaysis.ipynb.
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