When it comes to price prediction, the first thing we can think of is a competition on kaggle, about the prediction of housing prices, but in such a hot era of the real estate industry, it is also very interesting to make a Chinese version of housing price prediction, but what bloggers want to do is a pair of second-hand The prediction of equipment price is to predict the selling price by modeling various characteristics such as the type, age, used time, and geographical location of the second-hand equipment, but this is an easier application. Predicting the price of second-hand equipment with just a few pictures or a few pictures, I think this is more challenging.
The following price predictions are mainly constructed using the sk-learn package through linear regression models, decision tree (regression tree) models, xgboost, neural networks, support vector regression and other algorithm models.
1. Linear regression
from sklearn.linear_model import LinearRegression
2. Decision tree regression
from sklearn.tree import DecisionTreeRegressor
3. Support Vector Regression
from sklearn.svm import SVR linear_svr = SVR(kernel='linear') linear_svr.fit(x_train, y_train.ravel()) linear_svr_predict = linear_svr.predict(x_test)
4. Gradient Boosting Regression Algorithm
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
gbdt=GradientBoostingRegressor(
loss='ls'
, learning_rate=0.1
, n_estimators = 100
, subsample=1
, min_samples_split=2
, min_samples_leaf=1
, max_depth=3
, init=None
, random_state=None
, max_features=None
, alpha=0.9
, verbose=0
, max_leaf_nodes=None
, warm_start=False
)
train_feat=np.genfromtxt("train_feat.txt",dtype=np.float32)
train_id=np.genfromtxt("train_id.txt",dtype=np.float32)
test_feat=np.genfromtxt("test_feat.txt",dtype=np.float32)
test_id=np.genfromtxt("test_id.txt",dtype=np.float32)
print train_feat.shape，rain_id.shape，est_feat.shape，est_id.shape
gbdt.fit(train_feat,train_id)
pred=gbdt.predict(test_feat)
total_err=0
for i in range(pred.shape[0]):
print pred[i],test_id[i]
err=(pred[i]-test_id[i])/test_id[i]
total_err+=err*err
print total_err/pred.shape[0]
5. Random Forest Regression
from sklearn.ensemble import RandomForestRegressor data=[[0,0,0],[1,1,1],[2,2,2],[1,1,1],[2,2,2],[0,0,0]] target=[0,1,2,1,2,0] rf = RandomForestRegressor() rf.fit(data, target) print rf.predict([[1,1,1]]) print rf.predict([[1,1,1],[2,2,2]]) #[ 1.] #[ 1. 1.9] data2=[[0,0,0],[1,1,1],[2,2,2],[3,3,3],[4,4,4],[5,5,5]] target2=[0,1,2,3,4,5] rf2 = RandomForestRegressor() rf2.fit(data2, target2) print rf2.predict([[1,1,1]]) print rf2.predict([[1,1,1],[2,2,2],[4,4,4]]) #[ 0.7] #[ 0.7 1.8 4. ]
6. Linear regression of xgboost
import xgboost as xgb regr = xgb.XGBRegressor()
7. Neural Networks
You can use the neural network model to make predictions. This example is not available yet, and I will add it later.
Summary: This article mainly provides several options for rapid construction of price prediction models. Based on the above models, operations such as model fusion can also be done. For more in-depth understanding of some related content, refer to this article: Research on sales forecasting based on machine learning methods