To write a program to implement the the Logistic Regression Model to Predict the Placement Status of Student.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Jupyter notebook
- Get the data and use label encoder to change all the values to numeric.
- Drop the unwanted values,Check for NULL values, Duplicate values.
- Classify the training data and the test data.
- Calculate the accuracy score, confusion matrix and classification report.
Program to implement the the Logistic Regression Model to Predict the Placement Status of Student.
Developed by: Janani.S
RegisterNumber: 212222230049
import pandas as pd
data=pd.read_csv("Placement_Data.csv")
data.head()
data1=data.copy()
data1=data1.drop(["sl_no","salary"],axis=1)
data1.head()
data1.isnull().sum()
data1.duplicated().sum()
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
data1["gender"]=le.fit_transform(data1["gender"])
data1["ssc_b"]=le.fit_transform(data1["ssc_b"])
data1["hsc_b"]=le.fit_transform(data1["hsc_b"])
data1["hsc_s"]=le.fit_transform(data1["hsc_s"])
data1["degree_t"]=le.fit_transform(data1["degree_t"])
data1["workex"]=le.fit_transform(data1["workex"])
data1["specialisation"]=le.fit_transform(data1["specialisation"])
data1["status"]=le.fit_transform(data1["status"])
data1
x=data1.iloc[:,:-1]
x
y=data1["status"]
y
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression(solver="liblinear") # A Library for Large Linear Classification
lr.fit(x_train,y_train)
y_pred=lr.predict(x_test)
print("Predicted values : ")
y_pred
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test,y_pred)
print("Accuracy : ")
accuracy
from sklearn.metrics import confusion_matrix
confusion=confusion_matrix(y_test,y_pred)
print("Confusion matrix:\n",confusion)
from sklearn.metrics import classification_report
classification_report1 = classification_report(y_test,y_pred)
print("Classification Report : ")
print(classification_report1)
lr.predict([[1,80,1,90,1,1,90,1,0,85,1,85]])
Thus the program to implement the the Logistic Regression Model to Predict the Placement Status of Student is written and verified using python programming.