import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression

df=pd.read_csv('/content/minihomeprices (2).csv') print(df.head()) print(df.info()) print(df.describe()) print(df.isna().sum()) df['bedrooms']=df['bedrooms'].fillna(df['bedrooms'].median()) plt.figure(figsize=(7,7)) plt.title("Bedroom wise price increases") plt.xlabel("Bedrooms") plt.ylabel("Price") sns.barplot(x='bedrooms',y='price',data=df) plt.show() plt.figure(figsize=(7,7)) sns.lmplot(x='bedrooms',y='price',data=df) plt.title("price and bedroom wise lineplot") plt.xlabel("House Bedrooms") plt.ylabel("House Price") plt.show() x=df.drop(['price'],axis=1) y=df['price'] x['bedrooms']=x['bedrooms'].astype('int64') mdl=LinearRegression() mdl.fit(x,y)