prateekagr21 / Analysing-types-of-Hotels

Classifying different resorts and city hotels on their bookings and cancellation using various Machine Learning Algorithms

Analysing-types-of-Hotel

Classifying Resort and City Hotels Based on their bookings and Cancellation using Machine Learning Algorithms !

List of items you can take home with you from a hotel room- 'all for you xd'

Booking cancellations have significant impact on demand-management decisions in the hospitality industry.

To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation.

Now, as hotel chains consolidate, last-minute booking apps proliferate, and short-term rental sites like Airbnb and HomeAway grow, experts say the reservations landscape is undergoing an upheaval with new and higher fees and new restrictions on reservations that are driving new sources of revenue!!

A lot can be done with revenue management techniques when it comes to rates restrictions, like increasing the number of days until the arrival date that the customer can cancel without cost, giving you more time to resell the room.

But nowadays you have to apply similar restrictions to those applied by your competitive set and hotels around you, so if you are going to be stricter, costumers will prefer other hotels that are more permissive.

For Solving this Usecase, What I have done is :

• Collected the data and organized it to form a meaningful dataset.
• Checked for null values and took care of it.
• Observed the data to form meaningful insights!
  
  
• Did Exploratory Data Analysis on the dataset.
• Used correlations to form a heatmap.

For Visualizations, i used :

• Visualizations were made by using Matplotlib and Seaborn Libraries..!!

Did Data Pre-Processing and Feature Engineering :

• Made dummies for improving my model's Performance.
• One-hot-Encoding was Implemented.
• Made Binary Classifications Using Label Encoder and Standard Scaler
  To fit and transform Numerical and Categorical Column values.

And then I made my model for the Prediction :

• Did data processing
• Independent and Dependent Features.
• Did Train-Test split

Trained my Model using :

• Logistic Regresson
• Random Forest Classifier
• Ada Boost Classifier

Random Forest Classifier

• Predicted for the data
• Finded Accuracy score
• Plotted Confusion Matrix
• And at last, Classification report.
• And Analyzed the key factors responsible for prediction.

Logistic Regression

• Predicted for the data
• Finded Accuracy score
• Plotted Confusion Matrix
• And at last, Classification report.

Ada boost Classifier

• Predicted for the data
• Finded Accuracy score
• Plotted Confusion Matrix
• And at last, Classification report.

And for the conclusion -

From the above Three trained Models, It can be seen that
With the Accuracy of around 96%,
the Ada Boost model performed slightly better than rest of the Models.

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