Project: Weather Prediction Models

Overview

This project focuses on predicting weather conditions using various machine learning models. The models include Linear Regression, K-Nearest Neighbors (KNN), Decision Tree, Logistic Regression, and Support Vector Machine (SVM). The dataset used for this project is a weather dataset, processed and analyzed to predict future weather conditions.

In this project, The original source of the data is Australian Government's Bureau of Meteorology and the latest data can be gathered from http://www.bom.gov.au/climate/dwo/.

Path to Download Data:

https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillUp/labs/ML-FinalAssignment/Weather_Data.csv'

Installation

To run this project, you need Python installed on your machine. Additionally, install the following libraries:

• Pandas
• Scikit-learn
• Numpy

You can install them using pip:

pip install pandas scikit-learn numpy

Dataset

The dataset, Weather_Data.csv, contains various weather-related features. It is downloaded from a provided URL using the requests library. After downloading, the dataset undergoes preprocessing, including one-hot encoding of categorical variables and dropping unnecessary columns.

Models

The project implements the following machine learning models:

1. Linear Regression: Predicts continuous values.
2. K-Nearest Neighbors (KNN): A classification algorithm for categorizing data points based on their neighbors.
3. Decision Tree: A tree-like model for decision making.
4. Logistic Regression: Used for binary classification problems.
5. Support Vector Machine (SVM): A classification algorithm for finding the optimal hyperplane which best separates the classes.

Metrics

The models are evaluated based on various metrics:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• R-squared (R2)
• Accuracy Score
• Jaccard Index
• F1-Score
• Log Loss

These metrics help in determining the performance and suitability of each model for the given dataset.

Usage

1. Data Preparation: The script first downloads and processes the data.
2. Model Training: Each model is trained on the training dataset.
3. Prediction: The models make predictions on the test dataset.
4. Evaluation: Models are evaluated using the above metrics.

Sample Output

The results of the model evaluations are as follows:

Metric	Linear Regression	KNN	Tree	Logistic Regression	SVM
MAE	0.256315	NaN	NaN	NaN	NaN
MSE	0.115720	NaN	NaN	NaN	NaN
R2	0.427133	NaN	NaN	NaN	NaN
Accuracy Score	NaN	0.818321	0.818321	0.827481	0.722137
Jaccard Index	NaN	0.425121	0.480349	0.484018	0.722137
F1-Score	NaN	0.596610	0.648968	0.652308	0.605622
Log Loss	NaN	NaN	NaN	0.380085	NaN

Contribution

Contributions to this project are welcome. You can improve the models, add new ones, or enhance the data preprocessing steps. Please ensure to follow coding standards and comment your code where necessary.

License

This project is open-source and available under the MIT License.

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