Car Data Prediction- Machine Learning Program

 

In this post, we are gonna make a machine learning program using KNN model. For this, you have to install a IDE to write and compile the code in it. I have used VS Code in the program below, you also need to install python and libraries like pandas, numpy and sklearn in the command prompt. Here we are gonna do vehicle evaluation and you have to get car.data file from the link below:

Data Source : car.data

python file link : car data prediction model 

Preview:

  # these libraries are used in the program below.
 import pandas as pd 
 import numpy as np
 import sklearn
 from sklearn import neighbors, metrics
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import train_test_split



 # read the data we need for our prediction model
 data = pd.read_csv("C:\data\car.data")

 
 #copy all the needed values to x and y
 x= data[["buying","maint","safety"]].values
 y=data[["class"]]



 #converting the strings into unique values
 # y data
 label_mapping = {
    "unacc":0,
    "acc":1,
    "good":2,
    "vgood":3,
    
 }
 y["class"]=y["class"].map(label_mapping)
 y = np.array(y)


# arrange the X data

 Le= LabelEncoder()
 for i in range(len(x[0])):
   x[:,i]=Le.fit_transform(x[:,i])


 #create model
 # we have to first split the data into training and testing data using train_test_split function
 # here we are using KNN model to predict the y_test data

 knn= neighbors.KNeighborsClassifier(n_neighbors=25, weights="uniform")

 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

 knn.fit(x_train, y_train)

 prediction=knn.predict(x_test)


 accuracy= metrics.accuracy_score(y_test, prediction)
 print(accuracy)

 a=137  #enter any value of the y_test data and compare it with the predicted value of KNNN model.
 print("actual value : ", y[a])
 print("predicted value:", knn.predict(x)[a])