Explanation
1.
About me
2.
Introduction
2.1.
Context and objective
Getting Started
3.
Importing and installing libraries
4.
Importing and consolidating the databse
Treating database
5.
Steps underwent in treating the data
5.1.
Reducing excessive amount of columns
5.2.
Treating missing values
5.3.
Verifying data types of each collumn
6.
Treatment of outliers
6.1.
Analyzing heatmap
6.2.
Calculating limits
6.3.
Creating graphs
6.4.
Dropping unecessary columns
6.4.1.
'guests_included'
6.4.2.
'number_of_reviews'
6.4.3.
'maximum_nights'
6.5.
Outlier function
6.6.
Removing outliers from the 'price' and 'extra_people' columns
6.7.
Removing outliers of discrete numerical columns
7.
Treating non numerical values
7.1.
Group_categories function
7.1.1.
group property_type
7.1.2.
group bed_type
7.1.3.
group cancellation_policy
7.1.4.
group room_type
7.2.
Treating 'ammenities' collumn
Visualizing map
8.
Reducing visualized data
9.
Loading the map into the browser
Encoding
10.
Encoding explanation
11.
Encoding booleans
12.
Encoding text columns (dummy encoding)
Prediction models
13.
7 steps to build a prediction model
13.1.
Defining if it is classification or regression problem
13.2.
Choosing the metrics to evaluate the model
13.3.
Choose which models we are going to use
13.4.
Train the models and test
13.5.
Comparing the results of the models and choosing the best one
13.6.
Analyzing the best model
13.7.
Adjusting and improving the best model
14.
analyze_model function
15.
Splitting the data
16.
Testing the three models
Final model
17.
Choosing the best model
18.
Analyzing the best model
19.
Adjusting an improving the best model
20.
Applying the final changes
Deployment (streamlit)
21.
Deployment forms
22.
Exporting model as joblib
23.
airbnb_deploy file
23.1.
Importing streamlit and setting dictionaries
23.2.
Setting up page and config
24.
Creating the buttons
25.
Creating the preview value button
26.
Finalizing
Conclusion
27.
Thanks and future plans
Light
Rust
Coal
Navy
Ayu
Airbnb-Machine-Learning-Wiki
Treating non numerical values
We will analyze the columns without true or false and list values:
'property_type'
'bed_type'
'room_type'
'cancellation_policy'