Lab 1 Data Manipulation and Visualization using Python.ipynb

Kernel: Python 3 (ipykernel)

Analyze hotel data using python

Preparation:

• Load the hotel booking dataset into a Pandas DataFrame. What is the shape of the dataset?

• Print the first few rows of the dataset to understand its structure. What are the column names and data types?

• Check for missing values in the dataset. How many missing values are there in each column?

• Explore some basic statistics of numerical features in the dataset. What are the mean, median, minimum, and maximum values of the 'lead_time' feature?

• Explore some basic statistics of categorical features in the dataset. What are the unique values of the 'hotel' feature?

Processing:

• Impute missing values in numerical features using the mean strategy. How many missing values were imputed for each numerical feature?

• Impute missing values in categorical features using the most frequent strategy. Which values were imputed for each categorical feature?

• Convert categorical variables into dummy/indicator variables. How many new columns were added to the dataset after one-hot encoding?

• Standardize numerical features using StandardScaler. What are the mean and standard deviation of the 'lead_time' feature after standardization?

Analysis:

• Visualize the distribution of numerical features in the dataset. What insights can you gain from the visualizations?

• How many unique hotels are there in data?

• Create vizualtions to show reservation status.

• Explore the relationship between the 'lead_time' and 'is_canceled' features using a scatter plot. Is there any apparent correlation between these two features?

• Calculate the cancellation rate for each hotel. Which hotel has the highest cancellation rate?