├── README.md
└── code


/README.md:
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1 | # Climate-Change-Impact-Analysis
2 | Climate Change Impact Analysis is a Python-based application designed to explore and analyze datasets related to temperature, precipitation, and sea level rise. 
3 | 


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/code:
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 1 | # Climate-Change-Impact-Analysis
 2 | 
 3 | import pandas as pd
 4 | import numpy as np
 5 | import matplotlib.pyplot as plt
 6 | from sklearn.linear_model import LinearRegression
 7 | from sklearn.metrics import r2_score
 8 | 
 9 | # Load dataset (example: temperature and precipitation data)
10 | def load_data(file_path):
11 |     """Load climate data from a CSV file."""
12 |     data = pd.read_csv(file_path, parse_dates=['Date'])
13 |     data.set_index('Date', inplace=True)
14 |     return data
15 | 
16 | # Preprocess dataset
17 | def preprocess_data(data):
18 |     """Handle missing values and resample to yearly means."""
19 |     data = data.resample('Y').mean()
20 |     data = data.interpolate(method='linear')
21 |     return data
22 | 
23 | # Trend Analysis
24 | def trend_analysis(data, column):
25 |     """Perform linear regression to detect trends."""
26 |     X = np.array(range(len(data))).reshape(-1, 1)
27 |     y = data[column].values
28 |     model = LinearRegression()
29 |     model.fit(X, y)
30 |     trend = model.predict(X)
31 |     r2 = r2_score(y, trend)
32 |     return trend, r2
33 | 
34 | # Plot trends
35 | def plot_trends(data, column, trend):
36 |     """Plot the original data and the trend line."""
37 |     plt.figure(figsize=(10, 6))
38 |     plt.plot(data.index, data[column], label=f'Actual {column}')
39 |     plt.plot(data.index, trend, label=f'Trend line', linestyle='--')
40 |     plt.title(f'{column} Trend Analysis')
41 |     plt.xlabel('Year')
42 |     plt.ylabel(column)
43 |     plt.legend()
44 |     plt.grid(True)
45 |     plt.show()
46 | 
47 | # Main function
48 | def main():
49 |     file_path = 'climate_data.csv'  # Your CSV file path here
50 |     data = load_data(file_path)
51 |     data = preprocess_data(data)
52 | 
53 |     # Analyze Temperature
54 |     temp_trend, temp_r2 = trend_analysis(data, 'Temperature')
55 |     print(f"Temperature Trend R^2: {temp_r2:.2f}")
56 |     plot_trends(data, 'Temperature', temp_trend)
57 | 
58 |     # Analyze Precipitation
59 |     precip_trend, precip_r2 = trend_analysis(data, 'Precipitation')
60 |     print(f"Precipitation Trend R^2: {precip_r2:.2f}")
61 |     plot_trends(data, 'Precipitation', precip_trend)
62 | 
63 | if __name__ == "__main__":
64 |     main()
65 | 


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