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Climate Change Analysis- Time Series Forecasting in ML

  1. Objective:

    • Analyze climate change using temperature data and visualize trends over time.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Conclusion: