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Climate Change Analysis and Weather Forecasting

Learn to analyze patterns of climate change over the last six decades and make weather predictions.

Climate Change Analysis and Weather Forecasting

You will learn to:

Load and preprocess the historical daily temperature and precipitation data.

Understand the underlying patterns and trends of climate change over the last six decades.

Practice with various Python data visualization libraries.

Use Facebook’s time series forecasting library, Prophet, to make weather predictions.


Data Visualization

Data Science

Time Series Analysis


Intermediate knowledge of Python

Working knowledge of pandas

Good understanding of various Python data visualization libraries

Basic understanding of time series forecasting libraries







Project Description

Climate change refers to long-term shifts in temperature patterns and weather conditions on Earth, primarily caused by human activities, such as the burning of fossil fuels and deforestation. It leads to a variety of adverse effects, including rising global temperatures, melting ice caps, extreme weather events, and rises in sea level. Such changes disrupt ecosystems, threaten biodiversity, and impact human livelihoods. Analyzing climate change over the years and making weather predictions helps us understand the long-term impact of human activities on the environment, mitigate potential risks, and adapt to changing conditions, ensuring the well-being of ecosystems, economies, and human populations.

In this project, we’ll explore climate change patterns over a 60-year period, from 1960 to 2020, using a dataset containing temperature and precipitation data. We’ll leverage popular data analysis tools like pandas and seaborn to visualize and analyze the historical weather data. Additionally, we’ll implement Facebook’s Prophet, a time series forecasting library, to make predictions about weather conditions for the next five years. The project will help us gain valuable insights into long-term climate trends and enhance data analysis skills and forecasting using Python libraries, such as Matplotlib and Plotly.

Project Tasks


Get Started

Task 0: Introduction

Task 1: Import Libraries


Data Preprocessing

Task 2: Load the Dataset

Task 3: Convert the Date Column to the Datetime Format

Task 4: Get Unique City Names


Weather Analysis

Task 5: Plot the Average Daily Maximum and Minimum Temperatures

Task 6: Plot the Monthly Average Temperatures by Decade

Task 7: Plot the Average Yearly Precipitation by Cities

Task 8: Plot the Temperature and Moving Average by Cities


Weather Forecasting

Task 9: Select and Rename Relevant Columns

Task 10: Generate Weather Forecasts for the Next Five Years