🤡 Hazrat Ali

👹 Software Engineer || CEO and Founder HMSofttecH nnovation

🎳 Water Potability Check (ML) 🥰

This project predicts the potability of Water using machine learning models. After experimenting with multiple algorithms (Logistic Regression, Random Forest, Gradient Boosting, KNN, etc.), the final selected model was chosen based on accuracy, precision/recall, and overall robustness.

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📊 Dataset

• Source: Kaggle - Water Quality Dataset
• Features include:

- pH - Hardness - Solids - Chloramines - Sulfate - Conductivity - Organic Carbon - Trihalomethanes - Turbidity

• Target: Potability (0 = Not Potable, 1 = Potable)

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⚙️ Workflow

• Data Preprocessing

- Handling missing values - Normalization/standardization - Train-test split

• Model Selection

- Tested multiple models: - Logistic Regression With Plolynominal Feature Engineering - Logistic Regression Without Plolynominal Feature Engineering - Random Forest - Gradient Boosting - KNN - Compared performance using metrics: Accuracy, Precision, Recall, F1-score

• Final Model

- Selected model: [Logistic Regression With Plolynominal Feature Engineering] - Achieved best balance between accuracy and generalization.

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🚀 Colab Testing Phase

Colab link: Colab Test Phase - Water Quality Dataset