sans1012 / Hotel-Booking-model

Machine learning model used to predict the price of room in various months of year

Hotel-Booking-model

Machine learning model used to predict the price of room in various months of year

Steps Used

Cleaning the data and preparing the dataset for modelling

• Checking for Null Values
• Filling the null values with 0
• Filtering the data

Analysing the proprotion of guests in various countries

• Using chloropleth form plotly to represent the conclusion on map with color differentiation

Finding the amount payed for per night

• Finding amount variation according to hotel type: Resort, City

Finding Price Fluctation for per night per year

• Sorting data and Using a line plot to represent the conclusions

Finding the duration of stay

• Finding the count of people who booked the hotel but never arrived
• Using a bar plot to represent the same

Selecting important features

• Using Correlation
• Seperating numerical and categorical columns
• Extract derived features from fata
• Using Mean Encoding to convert string to int type data

Handling Outliers

Applying feature importance on data to select most impt features

Using Logistic Regression (Classification)

• Applying ML Algorithm and crossvalidate it

Using various Algorithms to find accuracy on the model

• Logistic Regression
• Random Forest
• Naive Bayes
• Decision Tree - Classifier
• KNN
• Finding the model which perfromed the best and model having the highest ccuracy.