Water Quality Analysis and Model Evaluation

The video tutorial explains the creation of a machine learning model for water quality analysis. It covers the objectives, data gathering from Kaggle, and data preparation using libraries like pandas and numpy. The tutorial details exploratory data analysis, including cleaning and analyzing the dataset. It then describes training and testing the model using a decision tree classifier, applying hyperparameter tuning, and evaluating the model's performance with training and testing scores.

What is the primary objective of the water quality analysis model?

• To predict if water is safe to drink

• To analyze the hardness of water

• To determine the chemical composition of water

• To measure the pH level of water

Which platform was used to gather the dataset for the model?

• Google Dataset

• Kaggle

• GitHub

• UCI Machine Learning Repository

Which library is NOT mentioned as being used in the initial steps of the model?

• pandas

• scikit-learn

• numpy

• matplotlib

What is the purpose of exploratory data analysis in this project?

• To train the model

• To gather more data

• To clean and analyze the data deeply

• To visualize the data

How many missing pH values were identified in the dataset?

• 0

• 491

• 781

• 162

What does a portability value of 1 indicate?

• Water is not safe to drink

• Water is hard

• Water is acidic

• Water is safe to drink

Which visualization technique was used to understand the data distribution?

• Line graph

• Pie chart

• Bar chart

• Distribution plot

What method is used to split the dataset into training and testing parts?

• K-fold

• Bootstrap

• Train-test split

• Cross-validation

Which classifier was used to train the model?

• Random Forest

• Decision Tree

• Logistic Regression

• Support Vector Machine

What was the testing accuracy of the model?

• 82%

• 59.6%

• 70%

• 60%