Water Potability Prediction Project

Objective:

The project aims to predict water potability based on water quality attributes using machine learning techniques.

Steps Taken:

1. Data Loading and Exploration:

   • Loaded the dataset from Water Quality and Potability on Kaggle and explored its features.
   • Checked for missing values and explored the distribution of the target variable.

2. Data Preprocessing:

   • Handled missing values through imputation.
   • Checked for outliers and decided to ignore them for the initial analysis.
   • Split the data into training and testing sets.
   • Scaled numerical features.

3. Modeling:

   • Trained individual models (Random Forest and Gradient Boosting).
   • Explored feature importance for model interpretation.
   • Created an ensemble model using the VotingClassifier.

4. Evaluation:

   • Evaluated the models using accuracy, precision, recall, and F1-score.
   • Compared the performance of individual models and the ensemble.

Key Findings:

• pH level and sulfate concentration are identified as the most influential features.
• The ensemble model showed improved accuracy and precision for predicting potable water.

Next Steps:

• Hyperparameter tuning for individual models.
• Explore feature engineering techniques.
• Experiment with different ensemble strategies.
• Revisit class imbalance handling techniques.

Dataset Source:

The dataset used in this project is sourced from Water Quality and Potability on Kaggle.

Feel free to contribute or reach out for further collaboration!