# Fitting dataset into Linear Regression model

Hi, today we will learn how to extract useful data from a large dataset and how to fit datasets into a linear regression model. We will do various types of operations to perform regression. Our main task to create a regression model that can predict our output. We will plot a graph of the best fit line (regression) will be shown. We will also find the Mean squared error, R2score. Finally, we will predict one sample. At first, we should know about what is Regression?

**What is regression?**

Basically, regression is a statistical term, regression is a statistical process to determine an estimated relationship of two variable sets.

In this diagram, we can fin red dots. They represent the price according to the weight. The blue line is the regression line.

## Python linear regression example with dataset

Let’s go for the coding section:

Requirements:

- Dataset :
- Numpy Library
- Pandas Library
- Matplotlib Library
- SKlearn Library (sci-kit learn)

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score,mean_squared_error %matplotlib inline df = pd.read_csv('car_data.csv') # Importing the dataset df.sample(5) #previewing dataset randomly

Output:

Then we import the car dataset. And print 5 sample dataset values. At first, we imported our necessary libraries.

print(df.shape) # view the dataset shape print(df['Make'].value_counts()) # viewing Car companies with their cars number

**output:**

Here we select only ‘Volkswagen’ cars from the large dataset. Because different types of cars have different brand value and higher or lower price. So we take only one car company for better prediction.

Then we view the shape and check if any null cell present or not. We found there are many null cells present. We delete those rows which have null cells. It is very important when you make a dataset for fitting any data model. Then we cross check if any null cells present or not. No null cell found then we print 5 sample dataset values.

new_df = new_df[['Engine HP','MSRP']] # We only take the 'Engine HP' and 'MSRP' columns new_df.sample(5) # Checking the random dataset sample

Output:

Engine HP | MSRP | |
---|---|---|

5423 | 292.0 | 40475 |

5467 | 170.0 | 22695 |

10539 | 240.0 | 52245 |

6037 | 210.0 | 24535 |

5342 | 200.0 | 24845 |

Here we select only 2 specific (‘Engine HP’ and ‘MSRP’) columns from all columns. It is very important to select only those columns which could be helpful for prediction. It depends on your common sense to select those columns. Please select those columns that wouldn’t spoil your prediction. After select only 2 columns, we view our new dataset.

X = np.array(new_df[['Engine HP']]) # Storing into X the 'Engine HP' as np.array y = np.array(new_df[['MSRP']]) # Storing into y the 'MSRP' as np.array print(X.shape) # Vewing the shape of X print(y.shape) # Vewing the shape of y

Output:

(581, 1) (581, 1)

Here we put the **‘Engine HP’** column as a numpy array into** ‘X’** variable. And **‘MSRP’** column as a numpy array into** ‘y’** variable. Then check the shape of the array.

plt.scatter(X,y,color="red") # Plot a graph X vs y plt.title('HP vs MSRP') plt.xlabel('HP') plt.ylabel('MSRP') plt.show()

Output:

Here we plot a scatter plot graph between **‘MSRP’** and **‘HP’. **After viewing this graph we ensured that we can perform a linear regression for prediction.

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state=15) # Spliting into train & test dataset regressor = LinearRegression() # Creating a regressior regressor.fit(X_train,y_train) # Fiting the dataset into the model

Output:

y_pred = regressor.predict(X_test) print('R2 score: %.2f' % r2_score(y_test,y_pred)) # Priniting R2 Score print('Mean squared Error :',mean_squared_error(y_test,y_pred)) # Priniting the mean error

Output:

R2 score: 0.73 Mean squared Error : 55796476.51179164

def car_price(hp): # A function to predict the price according to Horsepower result = regressor.predict(np.array(hp).reshape(1, -1)) return(result[0,0]) car_hp = int(input('Enter Volkswagen cars Horse Power : ')) print('This Volkswagen Prce will be : ',int(car_price(car_hp))*69,'₹')

Output:

Note: The whole code is available into jupyter notebook format (.ipynb) you can download/see this code. Link- Linear Regression-Car download

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Hello sir,

You have provided us a very useful article and i appreciate as you keep it in simple language.

But you haven’t check the problem of simultaneous, multicollinearity, hetroscedasticity etc.

add an example of outlier removal when the graph shows one?

You are tricking people into reading your article with bad SEO.

With just 11914 rows, its a joke at best to say that you are fitting large datasets. How about posting content with some actual value, say training a model on a dataset with 50 million rows and 100 columns.

The real challenge starts then.