Machine Learning

Create a box plot showing alcohol distribution by wine quality.

Calculate accuracy for a wine quality classifier (good/bad).

Calculate and print basic statistics (mean, std, min, max) for numerical columns in the Iris dataset.

Calculate and display the correlation matrix for the student performance dataset.

Calculate MAE for a linear regression model predicting house prices.

Add a new column 'price_per_sqft' to the house prices dataset and display the result.

Generate a classification report for the student activities classifier.

Generate a classification report for the Iris species prediction model.

Create a confusion matrix for the Iris species prediction model.

Create a confusion matrix for the student extra activities classifier.

Count and print the number of missing values in each column of the Titanic dataset.

Count and print the number of unique values in each column of the Iris dataset.

Create a bar chart showing average price by number of bedrooms.

Create a scatter plot of size_sqft vs price from the house prices dataset.

Convert the 'Pclass' column in Titanic dataset to dummy variables and print the result.

Calculate and print the cross-validation score for a logistic regression model on the Titanic dataset.

Train a decision tree to predict if students did extra activities based on their scores.

Train a decision tree classifier on the Iris dataset and print its depth.

Load the Titanic dataset and print its shape (rows, columns).

Convert the 'Sex' column in Titanic dataset to numerical values (male=0, female=1) and print the result.

Display feature importances from a random forest model trained on the Iris dataset.

Filter the Titanic dataset to show only female passengers and print the result.

Display houses with more than 3 bedrooms from the dataset.

Perform grid search to find the best K for KNN classifier on the Iris dataset.

Calculate the average final score by extra_activities status from the student dataset.

Fill missing age values in Titanic dataset with the mean age and print the updated Age column.

Train a KNN classifier to predict wine quality (threshold: >=7 is good) using alcohol and sulphates.

Train a linear regression model to predict diabetes progression and print the R2 score.

Load the house prices dataset and display only 'size_sqft' and 'price' columns.

Normalize the 'Age' column in Titanic dataset using min-max scaling and print the result.

Plot a histogram of final scores from the student dataset with 3 bins.

Create scatter plots of hours_studied vs final_score and previous_score vs final_score side by side.

Calculate precision and recall for the wine quality classifier.

Train a KNN classifier to predict iris species and print the accuracy.

Use linear regression to predict final_score based on hours_studied.

Train a random forest classifier on the Titanic dataset to predict survival.

Train a linear regression model to predict house price based on size.

Train a logistic regression model to predict survival on the Titanic dataset.

Split the Iris dataset into 70% training and 30% test sets and print their shapes.

Split the wine dataset into 80% train and 20% test sets and print their shapes.