Implementation of Regression Using CatBoost

We will use this dataset to perform a regression task using the catboost algorithm. But to use the catboost model we will first have to install the catboost package model using the below command:

Installing Packages

!pip install catboost

Importing Libraries and Dataset

Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.

• Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
• Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
• Matplotlib/Seaborn – This library is used to draw visualizations.
• Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.

Loading Dataset and Retriving Information

Output:

    Posted On  BHK   Rent  Size            Floor    Area Type  \
0  2022-05-18    2  10000  1100  Ground out of 2   Super Area   
1  2022-05-13    2  20000   800       1 out of 3   Super Area   
2  2022-05-16    2  17000  1000       1 out of 3   Super Area   
3  2022-07-04    2  10000   800       1 out of 2   Super Area   
4  2022-05-09    2   7500   850       1 out of 2  Carpet Area   
              Area Locality     City Furnishing Status  Tenant Preferred  \
0                    Bandel  Kolkata       Unfurnished  Bachelors/Family   
1  Phool Bagan, Kankurgachi  Kolkata    Semi-Furnished  Bachelors/Family   
2   Salt Lake City Sector 2  Kolkata    Semi-Furnished  Bachelors/Family   
3               Dumdum Park  Kolkata       Unfurnished  Bachelors/Family   
4             South Dum Dum  Kolkata       Unfurnished         Bachelors   
   Bathroom Point of Contact  
0         2    Contact Owner  
1         1    Contact Owner  
2         1    Contact Owner  
3         1    Contact Owner  
4         1    Contact Owner  

Here, we are loading the dataset and printing the top five rows in the datset.

Output:

(4746, 12)

Here, ‘df.shape’ prints the dimensions of the dataframe ‘df’.

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4746 entries, 0 to 4745
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   Posted On          4746 non-null   object
 1   BHK                4746 non-null   int64 
 2   Rent               4746 non-null   int64 
 3   Size               4746 non-null   int64 
 4   Floor              4746 non-null   object
 5   Area Type          4746 non-null   object
 6   Area Locality      4746 non-null   object
 7   City               4746 non-null   object
 8   Furnishing Status  4746 non-null   object
 9   Tenant Preferred   4746 non-null   object
 10  Bathroom           4746 non-null   int64 
 11  Point of Contact   4746 non-null   object
dtypes: int64(4), object(8)
memory usage: 445.1+ KB

Here, ‘df.info()’ displays the summary information about the dataframe ‘df’. It provides details such as no. of null-entries in each column, data types, and memory usage.

Output:

               BHK          Rent         Size     Bathroom
count  4746.000000  4.746000e+03  4746.000000  4746.000000
mean      2.083860  3.499345e+04   967.490729     1.965866
std       0.832256  7.810641e+04   634.202328     0.884532
min       1.000000  1.200000e+03    10.000000     1.000000
25%       2.000000  1.000000e+04   550.000000     1.000000
50%       2.000000  1.600000e+04   850.000000     2.000000
75%       3.000000  3.300000e+04  1200.000000     2.000000
max       6.000000  3.500000e+06  8000.000000    10.000000

Here, ‘df.describe()’ computes and displays basic statistical summary information for the numeric columns in the dataframe.

In this article, we will learn about one of the state-of-the-art machine learning models: Catboost here cat stands for categorical which implies that this algorithm is highly efficient when your data contains many categorical columns.