emanueleiacca/Playground-Series---Season-3-Episode-20

Predict CO2 Emissions in Rwanda # Kaggle Playground Competition: Predicting CO2 Emissions using Sentinel-5P Satellite Observations Welcome to the 2023 edition of Kaggle's Playground Series! This competition focuses on the important task of predicting future carbon emissions using machine learning models based on open-source CO2 emissions data from Sentinel-5P satellite observations. ## Competition Overview This competition aligns with the ongoing partnership between Kaggle and Zindi, aiming to drive community-driven impact across Africa. Zindi is a professional network for data scientists to learn, grow their careers, and contribute to impactful projects. The objective of this challenge is to leverage satellite observations to create machine learning models that predict carbon emissions, particularly in regions where on-the-ground monitoring is not feasible. For more details about the competition, you can visit the competition link:kaggle.com Since i could not upload the dataset of the competition because it was too big for Github, i'll link where you can download it: kaggle.com # Code Overview The provided code showcases a comprehensive pipeline for the competition task. Here's an overview of the key sections: ## Data Exploration and Visualization The code starts by reading the training data from a CSV file and converting the 'date' column to a datetime format. It performs initial data exploration by displaying the head, summary statistics, and information about the data. The code checks for missing values in the dataset and prints the count of missing values for each column. It generates boxplots and grouped boxplots to visualize the distribution of 'num_sold' (number of units sold) across different categories like country, store, and product. ## Time Series Analysis The code utilizes statistical tools and libraries like statsmodels and seaborn for time series analysis. It ca …