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:

   • 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.