[FIXED] Facing an issue while learning logical ML

Issue

When i run my code i am facing an error

AttributeError: 'DataFrame' object has no attribute '_validate_params'

Here is my code:

import seaborn as sb
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression as lr
from sklearn import metrics

lol = {"exp":[0,2,5,6,10,7,9,3,5,4,6,1,2,0,0,4,5,8,7,6],
    "sales":[10,20,15,12,14,18,19,20,25,10,5,7,8,20,15,17,18,25,14,17],
    "manage":[5,2,9,8,3,6,7,5,4,2,1,5,8,7,9,4,2,0,1,5],
    "get":[0,1,1,1,0,0,1,1,0,1,0,1,0,1,0,1,1,0,0,1]}
data = pd.DataFrame(lol,columns=["exp","sales","manage","get"])

x = data[["exp", "sales", "manage"]]
y = data["get"]

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)

lr.fit(x_train,y_train)
x_pred = lr.predict(x_test)

conf_mat = pd.crosstab(y_test,x_pred,rownames="True?",colnames="Pred")
sb.heatmap(conf_mat,annot=True)

print("Accuracy:" ,metrics.accuracy_score(y_test,x_pred))
plt.show()

Solution

The issues were with how you were importing LogisticRegression, and the syntax for pd.crosstab. See below for fixes.

import seaborn as sb
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics

data_dict = {"exp":[0,2,5,6,10,7,9,3,5,4,6,1,2,0,0,4,5,8,7,6],
    "sales":[10,20,15,12,14,18,19,20,25,10,5,7,8,20,15,17,18,25,14,17],
    "manage":[5,2,9,8,3,6,7,5,4,2,1,5,8,7,9,4,2,0,1,5],
    "get":[0,1,1,1,0,0,1,1,0,1,0,1,0,1,0,1,1,0,0,1]}
data = pd.DataFrame(data_dict)

x = data[["exp", "sales", "manage"]]
y = data["get"]

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)

lr = LogisticRegression() #Make a new LogisticRegression instance
lr.fit(x_train,y_train)   #Fit the new instance
x_pred = lr.predict(x_test)

conf_mat = pd.crosstab(index=y_test, columns=x_pred) #rows=y, columns=predictions
ax = sb.heatmap(conf_mat,annot=True, )
ax.set_xlabel('prediction')
ax.set_ylabel('label')
print("Accuracy:" ,metrics.accuracy_score(y_test,x_pred))
plt.show()

Answered By - some3128