2. GOVERNMENT ENGINEERING
COLLEGE, RAICHUR-584135
INTERNSHIP DOMAIN:ML
Internship
Work on
“Air Quality Prediction Using Linear Regression”
Name : Misba Nausheen
USN: 3GU20CS018
Under The Guidance
of
Dr. Shashikala Patil
HOD of CSE Dept
3. About the Company 4
Introduction
Existing System
Proposed System
5
6
7
Problem statement 8
Tools used 6
Tasks Performed 10
Results 11
Conclusion 12
References 13
Contents
4. The World Weather Repository (WWR) is a comprehensive and collaborative data hub that serves as
a crucial resource for climate scientists, meteorologists, policymakers, and researchers worldwide. This
repository plays a pivotal role in advancing our understanding of weather patterns, climate change,
and the development of accurate predictive models.
The WWR is a dynamic collection of meteorological and climatic data gathered from a multitude of
sources, including satellites, ground-based weather stations, and research institutions. It encompasses a
wide range of parameters, such as temperature, precipitation, wind speed, humidity, atmospheric
pressure, and more, spanning historical records and near-real-time observations. This extensive data
archive offers a valuable opportunity for in-depth analysis and research across various timescales and
geographical regions.
Abstract
5. About the company
ParvaM was founded by a team of
passionate folks from diverse platforms
with the intent of delivering the valued
services to keep the future ready with
various software solutions with cutting
edge technologies in the market.
ParvaM are here to deliver Quality
Technical Products, Development &
Services for all your Current and
Future software requirements.
6. Air quality is a critical environmental factor that profoundly affects human health, the ecosystem,
and overall quality of life.
The quality of the air we breathe is determined by the presence and concentration of various
pollutants, including particulate matter, gases like carbon monoxide and ozone, and volatile
organic compounds.
Poor air quality is associated with a range of health issues, including respiratory diseases,
cardiovascular problems, and even premature death.
Additionally, air pollution contributes to environmental degradation, climate change, and
economic losses.
Introduction
7. The current system often has a limited number of monitoring stations, which are typically
concentrated in urban areas. This results in inadequate coverage, especially in rural or remote
regions, where air quality issues may also exist.
Data collection from monitoring stations can be infrequent, leading to gaps in real-time
monitoring. Accessing air quality data and interpreting Air Quality Index (AQI) information may
not be user-friendly for the general public.
Some monitoring stations may lack the latest sensor technologies, making it challenging to
measure specific pollutants or detect emerging air quality concerns.
The existing air quality prediction system has several problems and limitations, which necessitate the
development of a more advanced and comprehensive system.
Here are some of the key problems and limitations of the current system:
Existing System
8. The proposed Air Quality Prediction System is designed to provide accurate and real-time air
quality predictions for various locations across the globe. Leveraging machine learning techniques,
data preprocessing, and a user-friendly interface, the system aims to offer valuable air quality
forecasts to both individual users and businesses relying on weather information for decision-
making.
The new system establishes an expanded network of strategically distributed monitoring stations,
ensuring comprehensive coverage across various geographic locations. This addresses the
limitation of data gaps and provides a more accurate representation of air quality conditions.
The proposed system features an intuitive web-based platform and mobile application with user-
friendly interfaces. Interactive maps, charts, and graphs allow users to visualize air quality data
easily. This addresses the limitation of limited accessibility and usability.
Proposed System
9. We are tasked with developing a predictive model to estimate the air quality that affects human
health and the overall well-being of a community. Monitoring and predicting air quality
parameters, such as ozone (O3) levels and carbon monoxide (CO) levels, is crucial for assessing
potential health risks and for environmental management.
The objective of this project is to develop a predictive model that can estimate the ozone (O3)
levels based on the levels of carbon monoxide (CO) in the air. This prediction can be valuable for
various applications, including health advisories, pollution control, and urban planning.
Objectives:
Problem Statement
10. Tools Used
Visual Studio Code (VS Code) is a highly popular, free,
open-source code editor developed by Microsoft. Launched
in 2015, it quickly gained widespread adoption among
developers of all backgrounds due to its flexibility,
performance, and rich feature set.
Python is an interpreted, high-level, general-purpose
programming language. Python interpreters are
available for many operating systems.
It is used for:
Web development (server-side).
Software development.
Mathematics.
System scripting.
11. Data Collection
Tasks Performed
Air quality prediction using linear regression is a method of estimating the
concentration of air pollutants in the future based on historical data and
meteorological factors. The tasks involved in this method are:
Data Cleaning Data Analysis Data Modelling Data Evaluation
13. Linear regression is a fundamental machine learning algorithm used for predicting a
continuous outcome variable (also called the dependent variable) based on one or more
predictor variables (independent variables). It's particularly useful for understanding and
modeling the relationship between variables and making predictions based on that
relationship.
Linear regression assumes that there's a linear relationship between the predictor variables
and the target variable. In a simple linear regression (with one predictor variable), this
relationship can be represented as:
• b is the intercept (the value of y when x is zero).
y = mx + b
Where
• y is the target variable (the variable we want to predict).
• x is the predictor variable (the variable used for prediction).
• m is the slope of the line (representing how y changes with a change in x).
Description of Algorithm
16. The " Air Quality Prediction using Linear Regression" project addresses the critical need for accurate
temperature forecasting. By applying linear regression techniques to historical weather data, this
project aims to contribute to the advancement of weather forecasting accuracy. Accurate temperature
predictions have wide-ranging applications, benefiting individuals, industries, and society as a whole.
This project holds the potential to enhance decision-making, improve resource allocation, and increase
overall preparedness for weather-related events. "Air Quality Prediction using Linear Regression" has
successfully demonstrated the potential of machine learning techniques, specifically linear regression,
to make accurate temperature forecasts based on historical weather data. This project has addressed
various key aspects and provided valuable insights into temperature prediction, with implications for
multiple sectors and everyday life.
Conclusion
