solangii / house-price-regression-problem

House Price of King County Region

House Price of King County Regions

Course project of MGE303 DataMining, UNIST

Implement : link

report : link

I will exploring the House Price of Kimg County Regions by term project of MGE303 DataMining class(2020, spring). Actually, This is a problem of regression. We want to know the house price in the King County area. So, Our goal is to more accurately predict house price for a house that meets the conditions i want by using the given data(Problem Definition).

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1. Data Munging

1. Data Load
2. Data Handle
3. Encoding
4. Histogram

2. EDA (Hypothesis Construction)

1. The relationship between PRICE and AREA

   Hypothesis 1:The relationship between PRICE and area variables is likely to have a major impact on variables AR_LIV, AR_RFTP and AVG_LIV_NEAR.

2. The relationship between PRICE and the number of room and floor(internal facilities)

   Hypothesis 2 : Looking at each case, the higher NUM_BD and BTH_STAT_2, the higher the tendency for PRICE. However, it does not seem to have much to do with NUM_FLR.

3. The relationship between Price and Climate change

   Hypothesis 3: Price, PRECIPITATION, Summer_HIGH, and Winter_Low are not related.

4. The relationship between Price and Near_RIV

   Hypothesis 4: If there is water near the house, the price is high. Therefore, NEAR_RIV affects PRICE.

5. The relationship between Price and Crime

   Hypothesis 5: Price, violent_crime, and Property_crime are not related.

6. The relationship between Price and house position(latitude, longitude)

   Hypothesis 6: Price, Longitude, and Latitude are not related.

7. The relationship between price and Appearance and Facilities

   Hypothesis 7: As I said earlier, the outlook and price seem to be related, and the condition was so focused on a particular value that it was difficult to find the relationship. Also, the price change depending on 'is_renovated' is not significant at less than 500,000. Therefore, price and IS_RENOVATED seem irrelevant. And there seems to be no change in PRICE according to YR_PAST.

Conclustion of Hypothesis :

• have relationship with PRICE : AR_LIV, AR_RFTP,AVG_AR_LIV_NEAR,NUM_BD,BTH_STAT_2,NEAR_RIV,OUTLOOK

• don't have relationship with PRICE : AR_PL, AR_BASE, AVG_AR_PL_NEAR, NUM_FLR, PRECEPITATION, SUMMER_HIGH, WINTER_LOW,VIOLENT_CRIME,PROPERTY_CRIME, LAT, LONG, CONDITION, IS_RENOVATED, YR_PAST

Construct correlation matrix

3. Modeling & Evaluate

• Multiple Linear Regression

  mlr = LinearRegression()
  mlr.fit(X_train,y_train)
  mlr_score = mlr.score(X_test,y_test)
  pred_mlr = mlr.predict(X_test)
  expl_mlr = explained_variance_score(pred_mlr,y_test)
  mse_mlr = mean_squared_error(pred_mlr,y_test)
  rmse_mlr = np.sqrt(mse_mlr)
  cv_mlr = cross_val_score(mlr,X_train,y_train, cv=10).mean()

• Decision Tree

  tr_regressor = DecisionTreeRegressor(random_state=0)
  tr_regressor.fit(X_train,y_train)
  tr_regressor.score(X_test,y_test)
  pred_tr = tr_regressor.predict(X_test)
  decision_score=tr_regressor.score(X_test,y_test)
  expl_tr = explained_variance_score(pred_tr,y_test)
  mse_tr = mean_squared_error(pred_tr,y_test)
  rmse_tr = np.sqrt(mse_tr)
  cv_tr = cross_val_score(tr_regressor,X_train,y_train, cv=10).mean()

• Random Forest Regression Model

  rf_regressor = RandomForestRegressor(n_estimators=28,random_state=0)
  rf_regressor.fit(X_train,y_train)
  rf_regressor.score(X_test,y_test)
  rf_pred =rf_regressor.predict(X_test)
  rf_score=rf_regressor.score(X_test,y_test)
  expl_rf = explained_variance_score(rf_pred,y_test)
  mse_rf = mean_squared_error(rf_pred,y_test)
  rmse_rf = np.sqrt(mse_rf)
  cv_rf = cross_val_score(rf_regressor,X_train,y_train, cv=10).mean()

Calculate Score

print("Multiple Linear Regression Model Score is ",round(mlr.score(X_test,y_test)*100))
print("Decision tree  Regression Model Score is ",round(tr_regressor.score(X_test,y_test)*100))
print("Random Forest Regression Model Score is ",round(rf_regressor.score(X_test,y_test)*100))
>> Multiple Linear Regression Model Score is  70.0
>> Decision tree  Regression Model Score is  76.0
>> Random Forest Regression Model Score is  88.0

4. Conclusion

models_score =pd.DataFrame({'Model':['Multiple Linear Regression','Decision Tree','Random forest Regression'],
                            'Score':[mlr_score,decision_score,rf_score],
                            'Explained Variance Score':[expl_mlr,expl_tr,expl_rf],
                            'RMSE':[rmse_mlr,rmse_tr,rmse_rf],
                            '10-CV':[cv_mlr,cv_tr,cv_rf]
                           })
models_score.sort_values(by='10-CV',ascending=False)

Of the three models, RMSE values are the smallest, and 10-CV and Score think the largest random forest is the most suitable model.

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