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https://github.com/parthds02/rain-prediction-in-australia-implementing-and-evaluating-multiple-classification-models

This project focuses on predicting rainfall in Australia using various classification models. It involves exploring and preprocessing the dataset, training multiple machine learning models, and evaluating their performance. The goal is to identify the best model for accurate rain prediction.
https://github.com/parthds02/rain-prediction-in-australia-implementing-and-evaluating-multiple-classification-models

classification-models coursera data-science jupyter-notebook machine-learning-algorithms model-evaluation python rainfall-prediction

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This project focuses on predicting rainfall in Australia using various classification models. It involves exploring and preprocessing the dataset, training multiple machine learning models, and evaluating their performance. The goal is to identify the best model for accurate rain prediction.

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README 

        
## Coursera Certification



I have completed the **IBM Machine Learning with Python** course on Coursera. You can view my certification below:



[View Certificate](https://www.coursera.org/account/accomplishments/verify/PXXD5XJ8YNJW)



## Project Overview



This project was completed as part of the Honors portion of the **IBM Machine Learning with Python course on Coursera**. The goal of the project was to build a classifier capable of predicting whether there will be rain the following day, based on historical weather data.



### Dataset



The dataset used for this project was sourced from the Australian Government's Bureau of Meteorology. It contains daily weather observations from 2008 to 2017. The dataset includes several weather-related features such as temperature, rainfall, humidity, wind speed, and more, along with the target variable, **RainTomorrow**, which indicates whether it will rain the next day.



You can access the dataset from the [Bureau of Meteorology](http://www.bom.gov.au/climate/dwo/) or from the Rattle repository [here](https://bitbucket.org/kayontoga/rattle/src/master/data/weatherAUS.RData).



### Key Features



The dataset contains the following columns:



| **Field**        | **Description**                                       | **Unit**         | **Type**   |

|------------------|-------------------------------------------------------|------------------|------------|

| Date             | Date of the observation                               | YYYY-MM-DD       | object     |

| Location         | Location of the observation                           | Location         | object     |

| MinTemp          | Minimum temperature                                  | Celsius          | float      |

| MaxTemp          | Maximum temperature                                  | Celsius          | float      |

| Rainfall         | Amount of rainfall                                   | Millimeters      | float      |

| Evaporation      | Amount of evaporation                                | Millimeters      | float      |

| Sunshine         | Amount of sunshine                                   | Hours            | float      |

| WindGustDir      | Direction of the strongest wind gust                 | Compass Points   | object     |

| WindGustSpeed    | Speed of the strongest wind gust                     | Kilometers/Hour  | float      |

| WindDir9am       | Wind direction before 9am                            | Compass Points   | object     |

| WindDir3pm       | Wind direction before 3pm                            | Compass Points   | object     |

| WindSpeed9am     | Wind speed before 9am                                | Kilometers/Hour  | float      |

| WindSpeed3pm     | Wind speed before 3pm                                | Kilometers/Hour  | float      |

| Humidity9am      | Humidity at 9am                                      | Percent          | float      |

| Humidity3pm      | Humidity at 3pm                                      | Percent          | float      |

| Pressure9am      | Atmospheric pressure at 9am                          | Hectopascal      | float      |

| Pressure3pm      | Atmospheric pressure at 3pm                          | Hectopascal      | float      |

| Cloud9am         | Fraction of the sky obscured by cloud at 9am         | Eighths          | float      |

| Cloud3pm         | Fraction of the sky obscured by cloud at 3pm         | Eighths          | float      |

| Temp9am          | Temperature at 9am                                   | Celsius          | float      |

| Temp3pm          | Temperature at 3pm                                   | Celsius          | float      |

| RainToday        | Whether it rained today                              | Yes/No           | object     |

| RainTomorrow     | Whether it will rain tomorrow                        | Yes/No           | float      |



### Methodology



In this project, I cleaned and preprocessed the dataset, then applied various machine learning classification algorithms to build a predictive model for rain prediction. The algorithms used include:



1. **Linear Regression**

2. **K-Nearest Neighbors (KNN)**

3. **Decision Trees**

4. **Logistic Regression**

5. **Support Vector Machines (SVM)**



### Evaluation Metrics



The performance of the models was evaluated using the following metrics:



1. Accuracy Score

2. Jaccard Index

3. F1-Score

4. Log Loss

5. Mean Absolute Error (MAE)

6. Mean Squared Error (MSE)

7. R²-Score



 Parth B Mistry