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--- |
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dataset_info: |
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features: |
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- name: Temperature |
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dtype: float64 |
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- name: Humidity |
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dtype: float64 |
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- name: Wind_Speed |
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dtype: float64 |
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- name: Cloud_Cover |
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dtype: float64 |
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- name: Pressure |
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dtype: float64 |
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- name: Rain |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 126558 |
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num_examples: 2500 |
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download_size: 120963 |
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dataset_size: 126558 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Weather Forecast Dataset |
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## Description |
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This dataset contains weather-related data collected for forecasting purposes. It includes various meteorological parameters that can be used for **climate analysis, weather prediction models,** and **machine learning applications** in forecasting. |
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## Dataset Details |
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### **Columns:** |
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- **Temperature**: Measured in degrees Celsius. |
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- **Humidity**: Percentage of atmospheric humidity. |
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- **Wind_Speed**: Speed of wind in meters per second. |
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- **Cloud_Cover**: Percentage of sky covered by clouds. |
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- **Pressure**: Atmospheric pressure in **hPa (hectopascal)**. |
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- **Rain**: Categorical label indicating whether it **rained (rain) or not (no rain)**. |
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### **Notes:** |
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- The dataset contains **2,500 entries**. |
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- The **Rain** column is categorical, making it useful for **classification models**. |
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- This dataset can be used for **time-series analysis** and **supervised learning tasks**. |
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## Use Cases |
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- **Predicting rainfall** using meteorological data. |
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- **Weather forecasting** using machine learning models. |
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- **Studying correlations** between **temperature, humidity, and pressure**. |
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## How to Use |
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You can load the dataset using the `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Tarakeshwaran/Hackathon_Weather_Forcast") |
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print(dataset) |
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