Search results
Explore and run machine learning code with Kaggle Notebooks | Using data from Rainfall in India.
- Sign In
Explore and run machine learning code with Kaggle Notebooks...
- Register
Kaggle is the world’s largest data science community with...
- Rainfall in India
Sub-division wise monthly data for 115 years from 1901-2015.
- Sign In
Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data.
- 🚀 Project Overview
- 📌 Key Features
- 🛠️ Built With
- 🎯 Application Scenarios
- 🔍 Inside the Code
This repository is dedicated to forecasting rainfall using a comprehensive dataset from the Australian Bureau of Meteorology. The dataset encompasses a decade's worth of daily weather observations, including metrics like temperature, humidity, pressure, and wind speed. The goal is to predict the likelihood of rain tomorrow ('RainTomorrow') based on...
•Extensive Weather Dataset: Leverages detailed weather data from 2008 to 2017, including various meteorological parameters.
•Data Preprocessing: Incorporates steps such as one-hot encoding for categorical variables, normalization, and data cleaning to prepare for model training.
•Multiple Classification Models: Applies various machine learning models like Logistic Regression, KNN, Decision Trees, and SVM for rain prediction.
•Model Evaluation: Models are evaluated based on metrics like Accuracy, Jaccard Index, F1-Score, and Log Loss.
•Python: For data manipulation and model building.
•Pandas & NumPy: For data handling and numerical computations.
•Scikit-Learn: For implementing various machine learning algorithms.
•Matplotlib: For visualizing data and results.
•Weather Forecasting: Useful for meteorologists and weather forecasting agencies.
•Risk Management: Assists in planning and decision-making processes for sectors dependent on weather conditions, like agriculture and logistics.
•Exploratory Data Analysis: Includes initial exploration of the dataset to understand the features and their characteristics.
•Feature Engineering: Demonstrates the process of transforming and encoding weather data features for model compatibility.
•Model Training and Prediction: Details the training process for each model and predictions made for rain occurrence.
•Performance Metrics Analysis: Comparative analysis of different models based on various performance metrics.
Jupyter Notebook 100.0%. This project utilizes a weather dataset to predict rainfall using machine learning algorithms. By analyzing the weather features such as temperature, humidity, wind speed, and pressure, the project aims to develop accurate models for rainfall prediction. - uyaditi/Rainfall-Prediction.
Jun 5, 2023 · Rainfall prediction is a common application of machine learning, and linear regression is a simple and effective technique that can be used for this purpose. In this task, the goal is to predict the amount of rainfall based on historical data.
Jun 25, 2020 · 19 Altmetric. Metrics. This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level.
People also ask
What is a rainfall dataset?
What is GitHub - uyaditi/Rainfall-prediction?
What is a rainfall forecast repository?
What is a weather dataset?
