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1. WEATHER
FORECASTING
PRESENTED BY:
Durgashankar puhan-220301120166
Subhrasis Biswal-220301120168
Kanta Anurag sahoo-220301120178
Adarsha Dalbehera-220301120183

2. CONTENTS
• INTRODUCTION
• OBJECTIVES
• DATA COLLECTION
• SOURCE OF DATA
• CONCLUSION

3. INTRODUCTION
 Weather forecasting is the process of predicting the
state of the atmosphere and its effects on Earth's
surface for a future time and specific location.
 The goal of weather forecasting is to provide
accurate and timely information about upcoming
weather conditions, which can help people prepare
for potential hazards such as severe storms, floods, or
heat wave
 Machine learning (ML) is increasingly being used in
weather forecasting to improve the accuracy of
weather predictions.

4. OBJECTIVES
 Data analysis: Machine learning algorithms can be used to
analyze vast amounts of data, including historical weather patterns,
satellite image, and weather station. By identifying predict future
weather patterns more accurately.
 Prediction models: Machine learning algorithms can be
used to develop weather prediction models that take into
account multiple variables such as temperature, humidity,
pressure, wind speed, and direction.
 Real-time forecasting: Machine learning algorithms can be
used to analyze real-time data from weather sensors to make
more accurate short-term weather predictions.

5. DATA COLLECTION
• ML PROJECT FINAL 2.xlsx
Microsoft Excel
Worksheet

6. SOURCE OF DATA
• https://www.visualcrossing.com/weathe
r-data
• https://www.kaggle.com/
• https://en.wikipedia.org/wiki/Weather_
forecasting

7. CONCLUSION
• In conclusion, weather forecasting is a
complex and challenging task that involves
analyzing vast amounts of data and making
predictions based on a variety of factors.
• However, there is still much work to be done
to improve the accuracy and reliability of
weather forecasting.

8. THANK YOU