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Car Price Prediction

Flight Fare Prediction

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Boston House Price Prediction

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Machine learning exercise 6: Boston House Price Prediction

Importing Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets
from sklearn.model_selection import
train_test_split
from xgboost import XGBRegressor
from sklearn import metrics

Getting the data

df1 = sklearn.datasets.load_boston()
df=pd.DataFrame(df1.data,columns=df1.feature_names)
df

Add the target (price) feature to the dataframe

df["Price"]=df1.target
df.head()

Gathering some information's

df.shape

df.describe()

df.info()

Let's create a dataset using the number of missing value present in the dataset and do sorting

x=[]
z=[]
for i in df:
    v=df[i].isnull().sum()/df.shape[0]*100
    x.append(v)
    z.append(i)
q={"Feature Name":z,"Percentage of missing values":x,}
missingPercentageDataset=pd.DataFrame(q).sort_values(by="Percentage of missing values",ascending=False)
pd.set_option("display.max_rows",None)
missingPercentageDataset

Correlation between features

correlation_1= df.corr()

plt.figure(figsize=(10,10))
sns.heatmap(correlation_1,cbar=True, square=True, fmt=".1f",annot=True,annot_kws={"size":8},cmap="Blues")

Separating data and labels

X=df.drop(["Price"],axis=1)
Y=df["Price"]

Splitting data into train and test data

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2, random_state=1)

raining model using XGBoost Regressor

For XGBoost Regressor we will only fit the model. No need to transform the model after fitting.

model=XGBRegressor()
model.fit(X_train,Y_train)

We can't use accuracy score for regression problem because all the values are numerical values. Here we will check or count the correctly predicted values by the model and then we will do comparison with the original value using r square, mean absolute error, etc error method. For regression problem these error methods are used and for classification problem this methods are used

Prediction on training data

training_data_prediction=model.predict(X_train)
training_data_prediction

Visualization of the actual value and training data predicted value

plt.scatter(Y_train,training_data_prediction)
plt.xlabel("actual value")
plt.ylabel("Predicted value")
plt.title("Actual value vs Predicted value")
plt.show()

R squared error training data

n training_data_prediction we have the prediction of our model and in Y_train we have the actual values of Price column. Let's use r2_score to see the error. By calculating the error we will be able to see that how much accurate result our model is giving

score_using_r2_score=metrics.r2_score(Y_train,training_data_prediction)
score_using_r2_score

Mean absolute error for training data

In training_data_prediction we have the prediction of our model and in Y_train we have the actual values of Price column. Let's use mean_absolute_error to see the error. By calculating the error we will be able to see that how much accurate result our model is giving.

score_using_mean_absolute_error=metrics.mean_absolute_error(Y_train,training_data_prediction)
score_using_mean_absolute_error

Prediction on the testing data

testing_data_prediction=model.predict(X_test)
testing_data_prediction

Visualization of the actual value and testing data predicted value

plt.scatter(Y_test,testing_data_prediction)
plt.xlabel("actual value")
plt.ylabel("Predicted value")
plt.title("Actual value vs Predicted value")
plt.show()

R squared error testing data

score_using_r2_score=metrics.r2_score(Y_test,testing_data_prediction)
score_using_r2_score

Mean absolute error for testing data

score_using_mean_absolute_error=metrics.mean_absolute_error(Y_test,testing_data_prediction)
score_using_mean_absolute_error

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