Rainfall Prediction Classifier Model

1. Project Overview

This project focuses on building and evaluating a machine learning model for a classification task. The primary objective is to accurately predict outcomes based on a given set of features. The repository contains the complete workflow, from initial data exploration and feature selection to model training, evaluation, and interpretation. A secondary goal was to use unsupervised learning to discover any inherent structures within the data.

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2. Methodology

The project follows a standard machine learning lifecycle, broken down into the following key stages:

a. Data Exploration & Feature Selection

The initial phase involved a thorough exploratory data analysis (EDA) to understand the dataset's characteristics.

• Correlation Analysis: A correlation matrix was generated to identify linear relationships between variables and the target outcome. This helped in understanding multicollinearity and identifying potentially strong predictors.
• Feature Importance: Tree-based models, specifically a Random Forest classifier, were used to calculate and rank feature importances. This provided a model-based view of which features contributed most significantly to the predictive power.

b. Model Development & Training

Several supervised learning algorithms were implemented to find the best-performing model for this classification problem. The models were chosen to represent different approaches to classification:

• Logistic Regression: Implemented as a baseline linear model.
• K-Nearest Neighbors (KNN): A non-parametric, instance-based learning model.
• Decision Tree: A simple tree-based model to capture non-linear patterns.
• Random Forest: An ensemble model to improve upon the Decision Tree by reducing variance and improving accuracy.

c. Performance Evaluation

Model performance was not judged on accuracy alone. A more nuanced evaluation was conducted to understand the model's behavior and error types.

• Confusion Matrix: Generated for each model to visualize performance.
• Key Metrics:
  • Precision: To measure the accuracy of positive predictions (minimize false positives).
  • Recall (Sensitivity): To measure the model's ability to identify all actual positives (minimize false negatives).
  • F1-Score: The harmonic mean of Precision and Recall, providing a single score that balances both metrics.

d. Unsupervised Learning: Clustering Analysis

To further explore the dataset, an unsupervised learning approach was applied.

• Algorithm: A clustering algorithm (e.g., K-Means) was used to segment the data into distinct groups without using pre-labeled outcomes.
• Optimal Cluster Selection: The Silhouette Score was calculated for a range of cluster numbers to determine the optimal k value, ensuring the resulting clusters were dense and well-separated.

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3. How to Use This Repository

1. Clone the repository:

   git clone <https://github.com/harry7747/Rainfall-Prediction-Classifier/tree/main>

2. Install dependencies:

   pip install -r requirements.txt

   (Note: You may need to create a requirements.txt file listing libraries like pandas, scikit-learn, numpy, matplotlib, etc.)
3. Run the Jupyter Notebook or Python script: The main analysis and model training code can be found in Rainfall_Prediction_Classifier.ipynb.

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4. Technologies Used

• Language: Python 3.13
• Libraries:
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn