🛠️ Tools and Technologies Used

🧠 Algorithms Used

• Linear Regression
• Ridge Regression
• Lasso Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Support Vector Machine (SVM)
• Grid Search with Cross-Validation (CV)
• Hard Voting

📏 Metrics Used

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

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📊 Exploratory Data Analysis (EDA)

In this project, extensive exploratory data analysis was conducted to understand the relationships between different weather variables and their impact on the prediction targets. Key steps included:

1. Pairplot Analysis

A pairplot was created using Seaborn to visualize the relationships between key meteorological variables:

• Columns: ['MinTemp', 'MaxTemp', 'Rainfall', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'RainToday', 'RainTomorrow']

Data Preprocessing

The data was preprocessed to prepare it for machine learning models. Key steps included:

• Mapping Categorical Variables to Numeric:

  df['RainToday'] = df['RainToday'].map({'No': 0, 'Yes': 1})
  df['RainTomorrow'] = df['RainTomorrow'].map({'No': 0, 'Yes': 1})
  

• Creating Dummy Variables:

  df_sydney_processed = pd.get_dummies(data=df, columns=['RainToday', 'WindGustDir', 'WindDir9am', 'WindDir3pm'])
  

🔍 Machine Learning Models and Results

Regression Algorithms

Several regression algorithms were used to predict weather outcomes. Here are the results:

Metric	Linear Regression	Ridge Regression	Lasso Regression
MAE	0.256318	0.254901	0.254754
MSE	0.115721	0.114630	0.114527
R2	0.427132	0.432530	0.433039

Classification Algorithms

Various classification algorithms were tested to predict the likelihood of rain:

• Decision Tree Classifier: Sample code for the Decision Tree model with hyperparameter tuning using Grid Search:

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

tree_model = DecisionTreeClassifier()
param_grid_tree = {
    'criterion': ['gini', 'entropy'],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10]
}
grid_search_tree = GridSearchCV(estimator=tree_model, param_grid=param_grid_tree, cv=5, scoring='accuracy')
grid_search_tree.fit(X_train, y_train)

Confusion Matrix for SVM:

A confusion matrix was created to visualize the performance of the Support Vector Machine (SVM) model:

Comparison of Metrics

Metric	KNN	Decision Tree	Logistic Regression	SVM
Accuracy	0.833588	0.792366	0.836641	0.836641
Jaccard Index	0.485849	0.428571	0.509174	0.497653
F1 Score	0.653968	0.600000	0.674772	0.664577
Log Loss	NaN	NaN	0.380451	NaN

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