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GamerBhai02

Jan 14th, 2025

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Python 1.25 KB | Source Code | 0 0

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import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
df = pd.read_csv(r"C:\Users\Abu Talha\Desktop\Housing Price.csv")
print(df.head())
X=df.iloc[:,:-1].values
Y=df.iloc[:,4].values
x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.7,test_size=0.3,random_state=0)
model = LinearRegression()
model.fit(x_train,y_train)
y_pred = model.predict(x_test)
dfp = pd.DataFrame({"Actual Price":y_test,"Predicted Price":y_pred})
print(dfp)
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
df = sns.load_dataset('iris')
print(df.head())
X=df.iloc[:,:-1].values
Y=df.iloc[:,4].values
x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.7,test_size=0.3,random_state=0)
model = RandomForestClassifier(n_estimators=50)
model.fit(x_train,y_train)
y_pred = model.predict(x_test)
from sklearn.metrics import confusion_matrix,accuracy_score,classification_report
print(confusion_matrix(y_test,y_pred))
print(accuracy_score(y_test,y_pred))
print(classification_report(y_test,y_pred))

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