Linear Regression

Predicted value is continuous
Author

Benedict Thekkel 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
path = Path('Data/homeprices.csv')
df = pd.read_csv(path)
df
area bedrooms age price
0 2600 3.0 20 550000
1 3000 4.0 15 565000
2 3200 NaN 18 610000
3 3600 3.0 30 595000
4 4000 5.0 8 760000
5 4100 6.0 8 810000 
plt.xlabel('area')
plt.ylabel('price')
plt.scatter(df.area,df.price,color='red',marker='+')

plt.show()

new_df = df.drop('price',axis='columns')
new_df = new_df.drop('bedrooms',axis='columns')
new_df = new_df.drop('age',axis='columns')
new_df
area
0 2600
1 3000
2 3200
3 3600
4 4000
5 4100 
price = df.price
price
0    550000
1    565000
2    610000
3    595000
4    760000
5    810000
Name: price, dtype: int64
from sklearn import linear_model
# Create linear regression object
reg = linear_model.LinearRegression()
reg.fit(new_df,price)
LinearRegression()
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reg.predict([[3300]])
/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
array([628813.88621022])
reg.coef_
array([167.30954677])
reg.intercept_
76692.3818707813

Y = m * X + b (m is coefficient and b is intercept)

5000*reg.coef_ + reg.intercept_
array([913240.11571842])
reg.predict([[5000]])
/home/ben/mambaforge/envs/cfast/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
array([913240.11571842])

Generate CSV file with list of home price predictions

Create a list of numbers from 1000 to 5000 with 500 increments

numbers_list = list(range(2000, 4000, 500))
# Create a DataFrame using the pandas constructor and a dictionary
data = {'area': numbers_list}
area_df = pd.DataFrame(data)
area_df
area
0 2000
1 2500
2 3000
3 3500 
p = reg.predict(area_df)
p
array([411311.47540984, 494966.2487946 , 578621.02217936, 662275.79556413])
area_df['prices']=p
area_df
area prices
0 2000 411311.475410
1 2500 494966.248795
2 3000 578621.022179
3 3500 662275.795564 
plt.xlabel('area')
plt.ylabel('price')
plt.scatter(df.area,
            df.price,
            color='red',
            marker='+')

plt.scatter(area_df.area,
            area_df.prices,
            color='green',
            marker='o')

plt.show()

area_df.to_csv("./Data/prediction.csv")
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