Weather Forecasting Project

📌 Overview

This project focuses on predicting weather conditions based on meteorological data. Using a dataset of daily weather observations, the project applies various Machine Learning algorithms to classify the weather into categories such as Rain, Sun, Drizzle, Snow, and Fog.

📂 Project Structure

• Model.ipynb: The Jupyter Notebook containing the end-to-end workflow: data preparation, visualization, preprocessing, and model training.
• seattle-weather.csv: The dataset containing historical weather data used for training and testing.

📊 Dataset Description

The Dataset consists of daily records with the following features:

Column	Description
date	The date of the observation
precipitation	Amount of precipitation
temp_max	Maximum temperature recorded
temp_min	Minimum temperature recorded
wind	Wind speed
weather	Target Variable (drizzle, rain, sun, snow, fog)

🛠️ Technologies & Libraries

The project is built using Python and the following libraries:

• Data Manipulation: pandas, numpy
• Visualization: seaborn, matplotlib
• Machine Learning (Scikit-Learn):
  • RandomForestClassifier
  • GradientBoostingClassifier
  • AdaBoostClassifier
  • LogisticRegression
  • SVC
  • DecisionTreeClassifier
• Preprocessing: imblearn (RandomOverSampler), StandardScaler

⚙️ Data Preprocessing

To improve model performance, the following preprocessing steps were applied:

1. Feature Engineering:

   • Extracted the Month from the date column to capture seasonal trends.
   • Dropped the original date column after extraction.

2. Handling Imbalanced Data:

   • Analyzed the target distribution and identified class imbalance (e.g., significantly more "Sun" or "Rain" days than "Snow").
   • Applied RandomOverSampler to balance the dataset, ensuring the model learns equally from all weather types.

3. Data Splitting:

   • Split the data into training and testing sets to evaluate performance on unseen data.

📈 Modeling & Evaluation

The notebook explores multiple classification algorithms. The primary workflow includes:

1. Training: Fitting models (like Random Forest) on the oversampled training data.
2. Evaluation: Using metrics such as Accuracy Score, Confusion Matrix, and Classification Report to assess performance.
3. Prediction: Testing the model with custom inputs (e.g., specific month, temp, and wind conditions) to predict the weather.

✅ Models Accuracy

🟢 Confusion Matrix

🚀 How to Run

1. Install the required dependencies:

   pip install pandas numpy seaborn matplotlib scikit-learn imbalanced-learn

2. Ensure seattle-weather.csv is in the project directory.
3. Open and run Model.ipynb to see the analysis and predictions.