1.LinearregressionML_PR.py

# -*- coding: utf-8 -*-
"""#DAY3  ML_LinearRegression interview.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1oxCgJ_K7Jwt3hhkbo7aAt-HiE1xi_xp_

# **DAY3:  ML_Linear Regression Interview**
- By PARIMAL A
- PRACTICAL
"""

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

from sklearn.linear_model import LinearRegression

!pip install scikit-learn==1.1.1

from sklearn.datasets import load_boston # housepricedataset

load_boston()

# or you canuse
#import seaborn as sns
## Load the Boston housing dataset
# boston = sns.load_dataset('boston')

df=load_boston()
df

dataset=pd.DataFrame(df.data)

dataset

dataset.columns=df.feature_names

dataset.head()

df.target.shape

df.feature_names.shape

## Independent features =X and dependent features=y
X=dataset
y=df.target

X

"""**Train test split**"""

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)

X_train

X_train.shape

X_test.shape

"""**Standardizing the dataset**"""

## standardizing the dataset : mean=0 std deviation =1
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

X_train=scaler.fit_transform(X_train)

X_test=scaler.transform(X_test)

"""## **Model Training**"""

from sklearn.linear_model import LinearRegression
##cross validation
from sklearn.model_selection import cross_val_score

regr=LinearRegression()
regr.fit(X_train,y_train) #training model x,y

mse=cross_val_score(regr,X_train,y_train,scoring='neg_mean_squared_error',cv=10)# mse

np.mean(mse) # shouldbeless

# Prediction
reg_pred=regr.predict(X_test)

reg_pred # ypred=y=mx+c

import seaborn as sns
sns.displot(reg_pred-y_test,kind='kde') # ypred- yactual

"""*Almost -10to -30 are more paameters fall so differenceisvery less*"""

from sklearn.metrics import r2_score

score=r2_score(reg_pred,y_test)

score

#save the model

import joblib

# Save the model to a file
joblib_file = "linear_regression_model.pkl"
joblib.dump(regr, joblib_file)
print(f"Model saved to {joblib_file}")

import pickle

# Save the model to a file
pickle_file = "linear_regression_modelpk.pkl"
with open(pickle_file, 'wb') as file:
    pickle.dump(regr, file)
print(f"Model saved to {pickle_file}")

# Compare predictions with actual values
print("Predicted values:", reg_pred) #ypredictedvalues
print("Actual values:", y_test) #yactualvalues

# Create a DataFrame to compare predictions with actual values
results_df = pd.DataFrame({'Actual': y_test, 'Predicted': reg_pred})

print(results_df)

# Predict y for a new given x value
predicted_y = regr.predict(X_test)[0] #first rowpredicted value

predicted_y

"""##**Day3 END**"""