https://github.com/sarangs1621/weather-prediction
Weather Prediction Using Machine Learning is a project that leverages machine learning algorithms to predict weather conditions based on historical data. It evaluates three popular ML models (Decision Tree, KNN, and Logistic Regression) and provides performance insights through metrics and visualizations.
https://github.com/sarangs1621/weather-prediction
data-analysis decision-tree jupyter-notebook knn logistic-regression machine-learning predictive-modeling python scikit-learn weather-prediction
Last synced: 2 months ago
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Weather Prediction Using Machine Learning is a project that leverages machine learning algorithms to predict weather conditions based on historical data. It evaluates three popular ML models (Decision Tree, KNN, and Logistic Regression) and provides performance insights through metrics and visualizations.
- Host: GitHub
- URL: https://github.com/sarangs1621/weather-prediction
- Owner: sarangs1621
- Created: 2024-11-26T18:45:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-26T18:56:43.000Z (over 1 year ago)
- Last Synced: 2025-03-20T07:49:09.835Z (over 1 year ago)
- Topics: data-analysis, decision-tree, jupyter-notebook, knn, logistic-regression, machine-learning, predictive-modeling, python, scikit-learn, weather-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌦️ Weather Prediction Using Machine Learning
Welcome to the **Weather Prediction Project**, where we use machine learning to analyze and predict weather conditions. This repository showcases the application of various ML algorithms and provides comprehensive insights into their performance.
---
## 📋 Table of Contents
1. [📖 Project Overview](#-project-overview)
2. [✨ Features](#-features)
3. [📊 Dataset](#-dataset)
4. [⚙️ Algorithms Used](#-algorithms-used)
5. [🛠️ Requirements](#️-requirements)
6. [🚀 Installation and Usage](#-installation-and-usage)
7. [📈 Results and Visualizations](#-results-and-visualizations)
8. [🤝 Contributors](#-contributors)
---
## 📖 Project Overview
The project aims to predict weather conditions using historical weather data. We apply various machine learning algorithms, evaluate their performance, and present the results with visualizations and metrics.
---
## ✨ Features
- Predict weather conditions with high accuracy.
- Implements three popular machine learning algorithms:
- **Decision Tree**
- **k-Nearest Neighbors (KNN)**
- **Logistic Regression**
- Detailed evaluation using metrics like **Accuracy**, **Precision**, **Recall**, and **F1 Score**.
- Visual insights for model performance.
---
## 📊 Dataset
- **Source**: [Kaggle](https://www.kaggle.com)
- **Features**:
- Temperature
- Humidity
- Wind Speed
- Precipitation Levels
- **Target**: Weather condition (e.g., Sunny, Rainy, Snowy)
---
## ⚙️ Algorithms Used
| Algorithm | Description |
|----------------------|-----------------------------------------------------------------------------|
| **Decision Tree** | A tree-like structure for decision-making and regression tasks. |
| **KNN** | Finds the majority label among k-nearest neighbors. |
| **Logistic Regression** | Predicts binary classes using statistical modeling. |
---
## 🛠️ Requirements
- **Python 3.8+**
- **Libraries**:
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Jupyter Notebook
---
## 🚀 Installation and Usage
1. Clone the repository:
```bash
git clone https://github.com/your-username/weather-prediction
```
2. Navigate to the project directory:
```bash
cd weather-prediction
```
3. Install the dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter notebooks for individual algorithms:
- [Decision Tree](Decision%20Tree.ipynb)
- [KNN](KNN.ipynb)
- [Logistic Regression](Logistic%20Regression.ipynb)
---
## 📈 Results and Visualizations
### 🔢 Performance Metrics
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) |
|----------------------|--------------|---------------|------------|--------------|
| **Decision Tree** | 98.0 | 97.5 | 97.8 | 97.6 |
| **KNN** | 88.0 | 87.5 | 88.2 | 87.8 |
| **Logistic Regression** | 85.0 | 84.7 | 85.3 | 85.0 |
### 📊 Accuracy Comparison
### 📉 Precision, Recall, and F1-Score
| Metric | Decision Tree | KNN | Logistic Regression |
|----------------|---------------|------|---------------------|
| **Precision** | 97.5% | 87.5% | 84.7% |
| **Recall** | 97.8% | 88.2% | 85.3% |
| **F1-Score** | 97.6% | 87.8% | 85.0% |
### 🔍 Feature Importance (Decision Tree)
---
## 📂 Directory Structure
```
weather-prediction/
│
├── Decision Tree.ipynb
├── KNN.ipynb
├── Logistic Regression.ipynb
├── images/
│ ├── accuracy_comparison.png
│ ├── feature_importance.png
│
├── README.md
├── requirements.txt
```
---
## 🤝 Contributors
- [Shradha](https://github.com/ShradhaShaji)
- [Sachin SM](https://github.com/sachin02-hub)
---
## 🖼️ Diagrams and Flowcharts
### ML Workflow Diagram
```mermaid
graph TD;
A[Data Collection] --> B[Data Preprocessing];
B --> C[Feature Engineering];
C --> D[Train-Test Split];
D --> E[Model Training];
E --> F[Model Evaluation];
F --> G[Deploy Model];
```
---