[FIXED] how can I convert Regression data into Classification data?

Issue

I have a data with columns

   ['symboling', 'Company', 'fueltype', 'aspiration', 'doornumber',
   'carbody', 'drivewheel', 'enginelocation', 'carlength', 'carwidth',
   'curbweight', 'enginetype', 'cylindernumber', 'enginesize',
   'fuelsystem', 'horsepower', 'price', 'total_mpg']

where the goal is to predict the price of car. Now he price data is continuous. I was wondering how can I convert it so that I can use classification model.

Upon searching I did found that I can do it by defining ranges but I am unable to understand it. Kindly help me

Solution

Let's suppose that we have a dataframe with 2 continuous columns, named x1 and x2:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

x1 = np.random.rand(100)
x2 = np.random.rand(100)
df = pd.DataFrame({"x1":x1,"x2":x2})
df.head()

#        x1       x2
#0  0.049202    0.131046
#1  0.606525    0.756687
#2  0.910932    0.944692
#3  0.904655    0.439637
#4  0.565204    0.418432

# Plot values
sns.scatterplot(x=range(100),y=df["x1"])
sns.scatterplot(x=range(100),y=df["x2"])

Then we can make some buckets like this:

x1_cat = pd.cut(df['x1'], bins=[0.,0.2,0.4,0.6,0.8,np.inf], labels=[0,1,2,3,4])
x2_cat = pd.cut(df['x2'], bins=[0.,0.2,0.4,0.6,0.8,np.inf], labels=[0,1,2,3,4])
df_cat = pd.concat([x1_cat,x2_cat],axis=1)
df_cat.head()

#   x1  x2
#0  0   0
#1  3   3
#2  4   4
#3  4   2
#4  2   2

# Plot values
sns.scatterplot(x=range(100),y=df_cat["x1"])
sns.scatterplot(x=range(100),y=df_cat["x2"])

Answered By - Kaveh