Weather prediction system project report with all coding part and flask web app using machine leanring?

About dataset. Project analysis report Notebook code Flask code with html.

About dataset The dataset you have provided contains daily weather information from 2012-01-01 to 2017-01-01 in a city or region. The features or columns of the dataset include:

date: The date of the weather record. precipitation: The amount of precipitation measured in millimeters (mm). temp_max: The maximum temperature recorded in degrees Celsius (°C). temp_min: The minimum temperature recorded in degrees Celsius (°C). wind: The wind speed recorded in meters per second (m/s). weather: A categorical variable indicating the type of weather (drizzle, rain, sunny, snow, etc.) The goal of your machine learning project is to predict the type of weather based on these features using Naive Bayes classification.

Report:

Introduction The purpose of this project is to develop a weather prediction system using the Naive Bayes algorithm. The system will be designed to forecast the weather for a given day based on historical weather data. The objective of this project is to create an accurate and reliable weather prediction system that can provide useful information to individuals and businesses in various industries. Preprocessing The preprocessing stage of this project is crucial to ensure that the data is in a suitable format for the machine learning algorithm to process. The main purpose of preprocessing is to clean the data and remove any anomalies or errors that could affect the accuracy of the model. Feature Engineering Feature engineering involves selecting and transforming the features in the dataset to improve the accuracy of the machine learning model. The purpose of feature engineering is to create informative features that capture the important characteristics of the data and remove any irrelevant features that may introduce noise. About the Dataset The dataset used in this project includes historical weather data such as precipitation, temperature, wind, and weather conditions. The purpose of this dataset is to provide the necessary data for the machine learning model to learn the relationships between the different weather variables and make accurate predictions.